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Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI. MATHEMATICS 2022. [DOI: 10.3390/math10040627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.
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Ciyamala Kushbu S, Inbamalar TM. Making Semi-Automatic Segmentation Method to be Automatic Using Deep Learning for Biventricular Segmentation. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Ventricular Segmentation or Delineation of Cardiac Magnetic Resonance Imaging (CMRI) is significant in obtaining the cardiac contractile function, which in turn is taken as input for diagnosing Cardio Vascular Diseases (CVD). Many automatic and semi-automatic methods were evolved to
meet the constraints of diagnosing CVDs. Among these, semi-automatic methods require user intervention for delineation of ventricles, which consumes time and leads to intra and inter-observability, as with manual delineation. Thus, the automatic method is suggested by most of the researchers
to address the above-stated problem. We proposed Saliency-based Active contour U-Net (SACU-Net) for automatic bi-ventricular segmentation which is found to surpass the existing highest developed methods regarding closeness to the gold standard. Three schemes are used by our proposed algorithm,
namely 1. Saliency Detection Scheme for Region of Interest (ROI) Localization to concentrate only on Object of Interest, 2. Drop-out embedded U-net for Initial Contour evolution that performs initial segmentation and 3. Local-Global-based Regional active Contour (LGRAC) to fine-tune and avoid
leaking, merging of ventricles during Delineation. We used three datasets namely Automatic Cardiac Diagnosing Challenge (ACDC) of MICCAI 2017, Right Ventricular Segmentation Challenge (RVSC) of MICCAI 2012, and Sunny Brook (SB) of MICCAI 2009 dataset to test the adaptability nature of our
algorithm over different scanner resolutions and protocols. 100 and 50 CMRI Images of ACDC were used for training and testing respectively which obtained average Dice Coefficient (DC) metric of 0.963, 0.934, and 0.948 for Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and
Right Ventricular Cavity (RVC) respectively. 32 and 16 CMRI Images of RVSC are used for preparing and experimenting respectively, which obtained an average DC metric of 0.95 for RVC.30 and 15 CMRI Images of SB are used for preparing and experimenting respectively, which obtained average DC
metric of 0.96 and 0.97 for LVC and LVM, respectively. Hausdorff Distance (HD) Metrics are also calculated to learn the distance of proposed delineated ventricles to reach the gold standard. The above resultant metrics show the robustness of our proposed SACU-Net in the segmentation of ventricles
of CMRI than previous methods.
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Affiliation(s)
- S. Ciyamala Kushbu
- Department of Information and Communication Engineering, Anna University, Chennai 25, Tamilnadu, India
| | - T. M. Inbamalar
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, Tiruvallur 601206, Tamilnadu, India
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3
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Bi K, Tan Y, Cheng K, Chen Q, Wang Y. Sequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:1591-1608. [PMID: 35135219 DOI: 10.3934/mbe.2022074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.
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Affiliation(s)
- Ke Bi
- School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China
| | - Yue Tan
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Ke Cheng
- School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China
| | - Qingfang Chen
- School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China
| | - Yuanquan Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
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4
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Pednekar AS, Cheong BYC, Muthupillai R. Ultrafast Computation of Left Ventricular Ejection Fraction by Using Temporal Intensity Variation in Cine Cardiac Magnetic Resonance. Tex Heart Inst J 2021; 48:471806. [PMID: 34643734 DOI: 10.14503/thij-20-7238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Cardiac magnetic resonance enables comprehensive cardiac evaluation; however, intense time and labor requirements for data acquisition and processing have discouraged many clinicians from using it. We have developed an alternative image-processing algorithm that requires minimal user interaction: an ultrafast algorithm that computes left ventricular ejection fraction (LVEF) by using temporal intensity variation in cine balanced steady-state free precession (bSSFP) short-axis images, with or without contrast medium. We evaluated the algorithm's performance against an expert observer's analysis for segmenting the LV cavity in 65 study participants (LVEF range, 12%-70%). In 12 instances, contrast medium was administered before cine imaging. Bland-Altman analysis revealed quantitative effects of LV basal, midcavity, and apical morphologic variation on the algorithm's accuracy. Total computation time for the LV stack was <2.5 seconds. The algorithm accurately delineated endocardial boundaries in 1,132 of 1,216 slices (93%). When contours in the extreme basal and apical slices were not adequate, they were replaced with manually drawn contours. The Bland-Altman mean differences were <1.2 mL (0.8%) for end-diastolic volume, <5 mL (6%) for end-systolic volume, and <3% for LVEF. Standard deviation of the difference was ≤4.1% of LV volume for all sections except the midcavity in end-systole (8.3% of end-systolic volume). We conclude that temporal intensity variation-based ultrafast LVEF computation is clinically accurate across a range of LV shapes and wall motions and is suitable for postcontrast cine SSFP imaging. Our algorithm enables real-time processing of cine bSSFP images on a commercial scanner console within 3 seconds in an unobtrusive automated process.
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Affiliation(s)
| | - Benjamin Y C Cheong
- Department of Radiology, CHI St. Luke's Health-Baylor St. Luke's Medical Center, Houston, Texas.,Department of Cardiology, Texas Heart Institute, Houston, Texas
| | - Raja Muthupillai
- Department of Radiology, CHI St. Luke's Health-Baylor St. Luke's Medical Center, Houston, Texas.,Department of Cardiology, Texas Heart Institute, Houston, Texas
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5
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Xue W, Li J, Hu Z, Kerfoot E, Clough J, Oksuz I, Xu H, Grau V, Guo F, Ng M, Li X, Li Q, Liu L, Ma J, Grinias E, Tziritas G, Yan W, Atehortúa A, Garreau M, Jang Y, Debus A, Ferrante E, Yang G, Hua T, Li S. Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data. IEEE J Biomed Health Inform 2021; 25:3541-3553. [PMID: 33684050 PMCID: PMC7611810 DOI: 10.1109/jbhi.2021.3064353] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm 2 for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.
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Affiliation(s)
- Wufeng Xue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
| | - Jiahui Li
- Beijing University of Post and Telecommunication, Beijing, China
| | | | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - James Clough
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Hao Xu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Fumin Guo
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Matthew Ng
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Lihong Liu
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Jin Ma
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Elias Grinias
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Georgios Tziritas
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Wenjun Yan
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Angélica Atehortúa
- LTSI UMR 1099, F-35000 Rennes, France; Universidad Nacional de Colombia, Bogotá, Colombia
| | | | - Yeonggul Jang
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University
| | - Alejandro Debus
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Enzo Ferrante
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Guanyu Yang
- Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Southeast University, Nanjing, China; LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
| | - Tiancong Hua
- Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs), Southeast University, Nanjing, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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6
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A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations. Med Image Anal 2021; 74:102210. [PMID: 34450467 DOI: 10.1016/j.media.2021.102210] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 06/29/2021] [Accepted: 08/04/2021] [Indexed: 11/20/2022]
Abstract
Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 24 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 109.7±12.2 ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. This novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches.
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7
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Luo Y, Xu L, Qi L. A cascaded FC-DenseNet and level set method (FCDL) for fully automatic segmentation of the right ventricle in cardiac MRI. Med Biol Eng Comput 2021; 59:561-574. [PMID: 33559862 DOI: 10.1007/s11517-020-02305-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/24/2020] [Indexed: 10/22/2022]
Abstract
Accurate segmentation of the right ventricle (RV) from cardiac magnetic resonance imaging (MRI) images is an essential step in estimating clinical indices such as stroke volume and ejection fraction. Recently, image segmentation methods based on fully convolutional neural networks (FCN) have drawn much attention and shown promising results. In this paper, a new fully automatic RV segmentation method combining the FC-DenseNet and the level set method (FCDL) is proposed. The FC-DenseNet is efficiently trained end-to-end, using RV images and ground truth masks to make a per-pixel semantic inference. As a result, probability images are produced, followed by the level set method responsible for smoothing and converging contours to improve accuracy. It is noted that the iteration times of the level set method is only 4 times, which is due to the semantic segmentation of the FC-DenseNet for RV. Finally, multi-object detection algorithm is applied to locate the RV. Experimental results (including 45 cases, 15 cases for training, 30 cases for testing) show that the FCDL method outperforms the U-net + level set (UL) and the level set methods that use the same dataset and the cardiac functional parameters are computed robustly by the FCDL method. The results validate the FCDL method as an efficient and satisfactory approach to RV segmentation.
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Affiliation(s)
- Yang Luo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China.,Anshan Normal University, Anshan, 114005, Liaoning, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China. .,Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, 110819, China. .,Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, 110169, China.
| | - Lin Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China
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8
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Automatic cardiac MRI segmentation and permutation-invariant pathology classification using deep neural networks and point clouds. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.08.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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9
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Soltanmohammadi P, Elwell J, Veeraraghavan V, Athwal GS, Willing R. Investigating the Effects of Demographics on Shoulder Morphology and Density Using Statistical Shape and Density Modeling. J Biomech Eng 2020; 142:121005. [PMID: 32601709 PMCID: PMC7580668 DOI: 10.1115/1.4047664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 06/17/2020] [Indexed: 11/08/2022]
Abstract
A better understanding of how the shape and density of the shoulder vary among members of a population can help design more effective population-based orthopedic implants. The main objective of this study was to develop statistical shape models (SSMs) and statistical density models (SDMs) of the shoulder to describe the main modes of variability in the shape and density distributions of shoulder bones within a population in terms of principal components (PCs). These PC scores were analyzed, and significant correlations were observed between the shape and density distributions of the shoulder and demographics of the population, such as sex and age. Our results demonstrated that when the overall body sizes of male and female donors were matched, males still had, on average, larger scapulae and thicker humeral cortical bones. Moreover, we concluded that age has a weak but significant inverse effect on the density within the entire shoulder. Weak and moderate, but significant, correlations were also found between many modes of shape and density variations in the shoulder. Our results suggested that donors with bigger humeri have bigger scapulae and higher bone density of humeri corresponds with higher bone density in the scapulae. Finally, asymmetry, to some extent, was noted in the shape and density distributions of the contralateral bones of the shoulder. These results can be used to help guide the designs of population-based prosthesis components and pre-operative surgical planning.
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Affiliation(s)
- Pendar Soltanmohammadi
- School of Biomedical Engineering, Western University, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Josie Elwell
- Department of Mechanical Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902-6000
| | - Vishnu Veeraraghavan
- Department of Mechanical Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902-6000
| | - George S. Athwal
- Roth | McFarlane Hand & Upper Limb Centre, St. Joseph's Health Care London, STN B, P.O. Box 5777, London, ON N6A 4V2, Canada
| | - Ryan Willing
- Department of Mechanical Engineering, Western University, 1151 Richmond Street, London, ON N6A 3K7, Canada
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Strocchi M, Augustin CM, Gsell MAF, Karabelas E, Neic A, Gillette K, Razeghi O, Prassl AJ, Vigmond EJ, Behar JM, Gould J, Sidhu B, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS One 2020; 15:e0235145. [PMID: 32589679 PMCID: PMC7319311 DOI: 10.1371/journal.pone.0235145] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
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Affiliation(s)
- Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | | | - Elias Karabelas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- University of Bordeaux, IMB, UMR 5251, F-33400 Talence, France
| | - Jonathan M. Behar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Christopher A. Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
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11
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Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation. Med Biol Eng Comput 2019; 57:2081-2092. [PMID: 31353427 DOI: 10.1007/s11517-019-01996-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the three-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model of their aortic root was constructed. The weights associated with the principal components and the volume of calcification in the aortic valve were used as parameters in a classification algorithm. The classification algorithm was trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. Cross validation showed that a random forest classifier assigned the same size in 65 ± 7% of the training cases, while 57 ± 8% of the patients with moderate to severe leakage were assigned a different size. This initial study showed that this semi-automatic method has the potential to correctly assign an implant size. Further research is required to assess whether the different size implants would improve the outcome of those patients.
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12
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Khened M, Kollerathu VA, Krishnamurthi G. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med Image Anal 2019; 51:21-45. [DOI: 10.1016/j.media.2018.10.004] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 10/11/2018] [Accepted: 10/18/2018] [Indexed: 10/28/2022]
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13
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A hybrid graph-based approach for right ventricle segmentation in cardiac MRI by long axis information transition. Phys Med 2018; 54:103-116. [DOI: 10.1016/j.ejmp.2018.09.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 09/16/2018] [Accepted: 09/22/2018] [Indexed: 11/17/2022] Open
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14
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Carminati M, Piazzese C, Pepi M, Tamborini G, Gripari P, Pontone G, Krause R, Auricchio A, Lang R, Caiani E. A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images. Comput Biol Med 2018; 96:241-251. [DOI: 10.1016/j.compbiomed.2018.03.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 03/21/2018] [Accepted: 03/21/2018] [Indexed: 10/17/2022]
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15
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Messadi M, Bessaid A, Mariano-Goulart D, Bouallègue FB. Development and clinical validation of a hybrid method for semiautomated left ventricle endocardial and epicardial boundary extraction on cine-magnetic resonance images. J Med Imaging (Bellingham) 2018; 5:024002. [PMID: 29662919 DOI: 10.1117/1.jmi.5.2.024002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 03/19/2018] [Indexed: 11/14/2022] Open
Abstract
We describe a hybrid method for left ventricle (LV) endocardial and epicardial segmentation on cardiac magnetic resonance (CMR) images requiring minimal operator intervention. Endocardium extraction results from the union of three independent estimations based on adaptive thresholding, region growing, and active contour with Chan-Vese energy function. Epicardium segmentation relies on conditional morphological dilation of the endocardial mask followed by active contour optimization. The proposed method was first evaluated using an open access database of 18 CMR for which expert manual contouring was available. The method was further validated on a retrospective cohort of 29 patients, who underwent CMR with expert manual segmentation. Regarding the open access database, similarity (Dice index) between hybrid and expert segmentations was good for end-diastolic (ED) endocardium (0.92), end-systolic (ES) endocardium (0.88), and ED epicardium (0.92). As for derived LV parameters, concordance (Lin's coefficient) was good for ED volume (0.91), ES volume (0.93), ejection fraction (EF; 0.89), and fair for myocardial mass (MM; 0.74). Regarding the retrospective patient study, concordance between expert and hybrid estimations was excellent for ED volume (0.95), ES volume (0.96), good for EF (0.86), and fair for MM (0.71). Hybrid segmentation resulted in small biases ([Formula: see text] for ED volume, [Formula: see text] for ES volume, [Formula: see text] for EF, and [Formula: see text] for MM) with little clinical relevance and acceptable for routine practice. The quickness and robustness of the proposed hybrid method and its ability to provide LV volumes, functions, and masses highly concordant with those given by expert segmentation support its pertinence for routine clinical use.
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Affiliation(s)
- Mahammed Messadi
- Aboubakr Belkaid University, Biomedical Engineering Department, Tlemcen, Algeria
| | - Abdelhafid Bessaid
- Aboubakr Belkaid University, Biomedical Engineering Department, Tlemcen, Algeria
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16
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Full left ventricle quantification via deep multitask relationships learning. Med Image Anal 2018; 43:54-65. [DOI: 10.1016/j.media.2017.09.005] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 08/25/2017] [Accepted: 09/18/2017] [Indexed: 12/22/2022]
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17
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Albà X, Lekadir K, Pereañez M, Medrano-Gracia P, Young AA, Frangi AF. Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med Image Anal 2017; 43:129-141. [PMID: 29073531 DOI: 10.1016/j.media.2017.10.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 08/29/2017] [Accepted: 10/04/2017] [Indexed: 01/09/2023]
Abstract
Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The results obtained based on over 1200 cases from the Cardiac Atlas Project show the promise of fully automatic initialization and quality control for population studies.
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Affiliation(s)
- Xènia Albà
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain.
| | - Karim Lekadir
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain
| | - Marco Pereañez
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Alejandro F Frangi
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK
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18
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Luo G, Dong S, Wang K, Zuo W, Cao S, Zhang H. Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images. IEEE Trans Biomed Eng 2017; 65:1924-1934. [PMID: 29035205 DOI: 10.1109/tbme.2017.2762762] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task. METHODS In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks. We study the end-to-end LV volume prediction method in items of the data preprocessing, network structure, and multiview fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new network structure for end-to-end LV volume estimation. Third, we explore the representational capacity of different slices and propose a fusion strategy to improve the prediction accuracy. RESULTS The evaluation results show that the proposed method outperforms other state-of-the-art LV volume estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth ( ${\rm{EDV:R}}^{{\rm 2}}={\text{0.974}}$, ${\rm{RMSE\,}}= {\text{9.6}}{\rm{\,ml}}$; ${\rm{ESV:R}}^{{\rm 2}}={\text{0.976}}$, ${\rm{RMSE}}= {\text{7.1}}\,{\text{ml}}$; ${\rm{EF:R}}^{{\rm 2}} ={\text{0.828}}$, ${\rm{RMSE}}= {\text{4.71}}\% $). CONCLUSION Experimental results prove that the proposed method may be useful for the LV volume prediction task. SIGNIFICANCE The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.
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Abstract
Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments' results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.
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Affiliation(s)
- Ali A Kiaei
- Department of computer engineering, Bu-Ali Sina University, Hamedan, Iran.
| | - Hassan Khotanlou
- Department of computer engineering, Bu-Ali Sina University, Hamedan, Iran.
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20
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Prediction of myocardial infarction by assessing regional cardiac wall in CMR images through active mesh modeling. Comput Biol Med 2017; 80:56-64. [DOI: 10.1016/j.compbiomed.2016.11.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 11/08/2016] [Accepted: 11/09/2016] [Indexed: 11/22/2022]
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21
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Islam A, Bhaduri M, Chan I. Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2174-2188. [PMID: 27093546 DOI: 10.1109/tmi.2016.2553153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The diagnosis, comparative and population study of cardiac radiology data require heart segmentation on increasingly large amount of images from different modalities/chambers/patients under various imaging views. Most existing automatic cardiac segmentation methods are often limited to single image segmentation with regulated modality/region settings or well-cropped ROI areas, which is impossible for large datasets due to enormous device protocols and institutional differences. A pure data-driven unsupervised segmentation without regulated setting requirements is crucial in this scenario, and will significantly automate the manual work and adopt the various changes of modality, subject or view. In this paper, we propose a general unsupervised groupwise segmentation: a direct simultaneous segmentation for a group of multi-modality, multi-chamber, multi-subject ( M3) cardiac images from a freely chosen imaging view. The segmentation can directly perform not only on regulated two/four-chamber images, but also on non-regulated uncropped raw MR/CT scans. A new Synchronized Spectral Network (SSN) is developed for the simultaneous decomposing, synchronizing, and clustering the spectral features of free-view M3 cardiac images. The SSN-based groupwise analysis of image spectral bases immediately leads to groupwise segmentation of M3 freeview images. The segmentation is quantitatively evaluated by three datasets (MR and CT mixed) with more than 200 subjects. High dice metric ( ) is consistently achieved in validation. Our method provides a powerful tool for medical images under general imaging environment.
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22
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Santiago C, Nascimento JC, Marques JS. A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2337-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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23
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Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation. Med Image Anal 2016; 30:120-129. [DOI: 10.1016/j.media.2015.07.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 03/29/2015] [Accepted: 07/11/2015] [Indexed: 12/19/2022]
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24
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Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2016; 29:155-95. [PMID: 26811173 PMCID: PMC4830888 DOI: 10.1007/s10334-015-0521-4] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.
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Affiliation(s)
- Peng Peng
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | | | - Ali Gooya
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | - Ling Shao
- Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Steffen E Petersen
- Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
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25
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Albà X, Pereañez M, Hoogendoorn C, Swift AJ, Wild JM, Frangi AF, Lekadir K. An Algorithm for the Segmentation of Highly Abnormal Hearts Using a Generic Statistical Shape Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:845-859. [PMID: 26552082 DOI: 10.1109/tmi.2015.2497906] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Statistical shape models (SSMs) have been widely employed in cardiac image segmentation. However, in conditions that induce severe shape abnormality and remodeling, such as in the case of pulmonary hypertension (PH) or hypertrophic cardiomyopathy (HCM), a single SSM is rarely capable of capturing the anatomical variability in the extremes of the distribution. This work presents a new algorithm for the segmentation of severely abnormal hearts. The algorithm is highly flexible, as it does not require a priori knowledge of the involved pathology or any specific parameter tuning to be applied to the cardiac image under analysis. The fundamental idea is to approximate the gross effect of the abnormality with a virtual remodeling transformation between the patient-specific geometry and the average shape of the reference model (e.g., average normal morphology). To define this mapping, a set of landmark points are automatically identified during boundary point search, by estimating the reliability of the candidate points. With the obtained transformation, the feature points extracted from the patient image volume are then projected onto the space of the reference SSM, where the model is used to effectively constrain and guide the segmentation process. The extracted shape in the reference space is finally propagated back to the original image of the abnormal heart to obtain the final segmentation. Detailed validation with patients diagnosed with PH and HCM shows the robustness and flexibility of the technique for the segmentation of highly abnormal hearts of different pathologies.
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26
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Wan M, Huang W, Zhang JM, Zhao X, Tan RS, Wan X, Zhong L. Variational Reconstruction of Left Cardiac Structure from CMR Images. PLoS One 2015; 10:e0145570. [PMID: 26689551 PMCID: PMC4699201 DOI: 10.1371/journal.pone.0145570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 12/04/2015] [Indexed: 11/20/2022] Open
Abstract
Cardiovascular Disease (CVD), accounting for 17% of overall deaths in the USA, is the leading cause of death over the world. Advances in medical imaging techniques make the quantitative assessment of both the anatomy and function of heart possible. The cardiac modeling is an invariable prerequisite for quantitative analysis. In this study, a novel method is proposed to reconstruct the left cardiac structure from multi-planed cardiac magnetic resonance (CMR) images and contours. Routine CMR examination was performed to acquire both long axis and short axis images. Trained technologists delineated the endocardial contours. Multiple sets of two dimensional contours were projected into the three dimensional patient-based coordinate system and registered to each other. The union of the registered point sets was applied a variational surface reconstruction algorithm based on Delaunay triangulation and graph-cuts. The resulting triangulated surfaces were further post-processed. Quantitative evaluation on our method was performed via computing the overlapping ratio between the reconstructed model and the manually delineated long axis contours, which validates our method. We envisage that this method could be used by radiographers and cardiologists to diagnose and assess cardiac function in patients with diverse heart diseases.
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Affiliation(s)
- Min Wan
- Nanchang University, Nanchang, Jiangxi Province, P.R.China 330031
- * E-mail: (MW); (LZ)
| | - Wei Huang
- Nanchang University, Nanchang, Jiangxi Province, P.R.China 330031
| | - Jun-Mei Zhang
- National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore
- Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore 169857, Singapore
| | - Xiaodan Zhao
- National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore
| | - Ru San Tan
- National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore
- Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore 169857, Singapore
| | - Xiaofeng Wan
- Nanchang University, Nanchang, Jiangxi Province, P.R.China 330031
| | - Liang Zhong
- National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore
- Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore 169857, Singapore
- * E-mail: (MW); (LZ)
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27
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Bai W, Shi W, de Marvao A, Dawes TJW, O'Regan DP, Cook SA, Rueckert D. A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med Image Anal 2015; 26:133-45. [PMID: 26387054 DOI: 10.1016/j.media.2015.08.009] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 07/24/2015] [Accepted: 08/20/2015] [Indexed: 11/30/2022]
Abstract
Atlases encode valuable anatomical and functional information from a population. In this work, a bi-ventricular cardiac atlas was built from a unique data set, which consists of high resolution cardiac MR images of 1000+ normal subjects. Based on the atlas, statistical methods were used to study the variation of cardiac shapes and the distribution of cardiac motion across the spatio-temporal domain. We have shown how statistical parametric mapping (SPM) can be combined with a general linear model to study the impact of gender and age on regional myocardial wall thickness. Finally, we have also investigated the influence of the population size on atlas construction and atlas-based analysis. The high resolution atlas, the statistical models and the SPM method will benefit more studies on cardiac anatomy and function analysis in the future.
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Affiliation(s)
- Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK.
| | - Wenzhe Shi
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Antonio de Marvao
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Timothy J W Dawes
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Declan P O'Regan
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Stuart A Cook
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK; National Heart Centre Singapore, Singapore, Duke-NUS Graduate Medical School, Singapore
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
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28
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Mahapatra D. Automatic cardiac segmentation using semantic information from random forests. J Digit Imaging 2015; 27:794-804. [PMID: 24895064 DOI: 10.1007/s10278-014-9705-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology, CAB E65.1, Universitatstrasse 6, Zurich, 8092, Switzerland,
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29
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Zhuang X, Bai W, Song J, Zhan S, Qian X, Shi W, Lian Y, Rueckert D. Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Med Phys 2015; 42:3822-33. [DOI: 10.1118/1.4921366] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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30
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Abstract
High-level noise and low contrast characteristics in medical images continue to present major bottlenecks in their segmentation despite increased imaging modalities. This paper presents a semi-automatic algorithm that utilizes the noise for enhancing the contrast of low contrast input magnetic resonance images followed by a new graph cut method to reconstruct the surface of left ventricle. The main contribution in this work is a new formulation for preventing the conventional cellular automata method to leak into surrounding regions of similar intensity. Instead of segmenting each slice of a subject sequence individually, we empirically select a few slices, segment them, and reconstruct the left ventricular surface. During the course of surface reconstruction, we use level sets to segment the rest of the slices automatically. We have throughly evaluated the method on both York and MICCAI Grand Challenge workshop databases. The average Dice coefficient (in %) is found to be 92.4 ± 1.3 (value indicates the mean and standard deviation) whereas false positive ratio, false negative ratio, and specificity are found to be 0.019, 7.62 × 10-3, and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.94 mm. The encouraging quantitative and qualitative results reflect the potential of the proposed method.
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31
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Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online 2015; 14:35. [PMID: 25928297 PMCID: PMC4424572 DOI: 10.1186/s12938-015-0033-5] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 04/02/2015] [Indexed: 01/19/2023] Open
Abstract
The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.
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Affiliation(s)
- Alejandro Lopez-Perez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
| | - Rafael Sebastian
- Computational Multiscale Physiology Lab (CoMMLab), Universitat de València, València, Spain.
| | - Jose M Ferrero
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
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32
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Punithakumar K, Noga M, Ben Ayed I, Boulanger P. Right ventricular segmentation in cardiac MRI with moving mesh correspondences. Comput Med Imaging Graph 2015; 43:15-25. [PMID: 25733395 DOI: 10.1016/j.compmedimag.2015.01.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 11/27/2014] [Accepted: 01/09/2015] [Indexed: 11/25/2022]
Abstract
This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84±0.11 for the epicardium and 0.79±0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82±0.14, which demonstrates the robustness of the algorithm.
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Affiliation(s)
- Kumaradevan Punithakumar
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
| | - Michelle Noga
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Ismail Ben Ayed
- GE Healthcare, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Pierre Boulanger
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
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33
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Petitjean C, Zuluaga MA, Bai W, Dacher JN, Grosgeorge D, Caudron J, Ruan S, Ayed IB, Cardoso MJ, Chen HC, Jimenez-Carretero D, Ledesma-Carbayo MJ, Davatzikos C, Doshi J, Erus G, Maier OM, Nambakhsh CM, Ou Y, Ourselin S, Peng CW, Peters NS, Peters TM, Rajchl M, Rueckert D, Santos A, Shi W, Wang CW, Wang H, Yuan J. Right ventricle segmentation from cardiac MRI: A collation study. Med Image Anal 2015; 19:187-202. [DOI: 10.1016/j.media.2014.10.004] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 10/09/2014] [Accepted: 10/13/2014] [Indexed: 10/24/2022]
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Bonaretti S, Seiler C, Boichon C, Reyes M, Büchler P. Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations. Med Eng Phys 2014; 36:1626-35. [DOI: 10.1016/j.medengphy.2014.09.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 09/01/2014] [Accepted: 09/07/2014] [Indexed: 10/24/2022]
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A method for generating large datasets of organ geometries for radiotherapy treatment planning studies. Radiol Oncol 2014; 48:408-15. [PMID: 25435856 PMCID: PMC4230563 DOI: 10.2478/raon-2014-0003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Accepted: 10/11/2013] [Indexed: 11/25/2022] Open
Abstract
Background With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy. Methods Given a training dataset of organ shapes derived from daily cone-beam CT, we align them into a common coordinate frame and select one of the training surfaces as reference surface. A statistical shape model of organs was constructed, based on the establishment of point correspondence between surfaces and non-uniform rational B-spline (NURBS) representation. A principal component analysis is performed on the sampled surface points to capture the major variation modes of each organ. Results A set of principal components and their respective coefficients, which represent organ surface deformation, were obtained, and a statistical analysis of the coefficients was performed. New sets of statistically equivalent coefficients can be constructed and assigned to the principal components, resulting in a larger geometry dataset for the patient’s organs. Conclusions These generated organ geometries are realistic and statistically representative.
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Caiani EG, Colombo A, Pepi M, Piazzese C, Maffessanti F, Lang RM, Carminati MC. Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes. Europace 2014; 16 Suppl 4:iv96-iv101. [PMID: 25362176 DOI: 10.1093/europace/euu232] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
AIMS To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with 'gold standard' manual tracings and discs-summation method. METHODS AND RESULTS The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland-Altman analyses were applied vs. the manual 'gold standard'. Active shape modelling results showed high correlations with manual values both for LV volumes (r(2) > 0.98) and ejection fraction (EF) (r(2) > 0.90), non-significant biases and narrow limits of agreement. CONCLUSION The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling.
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Affiliation(s)
- Enrico G Caiani
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci, 32, 20133 Milan, Italy
| | - Andrea Colombo
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci, 32, 20133 Milan, Italy
| | - Mauro Pepi
- IRCCS Centro Cardiologico Monzino, 20138 Milan, Italy
| | - Concetta Piazzese
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci, 32, 20133 Milan, Italy Center for Computational Medicine in Cardiology, Universitá della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Francesco Maffessanti
- Noninvasive imaging Laboratories, University of Chicago Medical Center, 60637 Chicago, USA
| | - Roberto M Lang
- Noninvasive imaging Laboratories, University of Chicago Medical Center, 60637 Chicago, USA
| | - Maria Chiara Carminati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci, 32, 20133 Milan, Italy IRCCS Centro Cardiologico Monzino, 20138 Milan, Italy
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Arif O, Sundaramoorthi G, Hong BW, Yezzi A. Tracking using motion estimation with physically motivated inter-region constraints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1875-1889. [PMID: 24846558 DOI: 10.1109/tmi.2014.2325040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous estimate of the structures using a new physically motivated motion estimation scheme. Our method estimates motion by regularizing only within structures so that differing motions among different structures are not mixed. It simultaneously satisfies the physical constraints at the interface between a fluid and a medium that the normal component of the fluid's motion must match the normal component of the medium's motion and the No-Slip condition, which states that the tangential velocity approaches zero near the interface. We show that these conditions lead to partial differential equations with Robin boundary conditions at the interface, which couple the motion between structures. We show that propagating a segmentation across frames using our motion estimation scheme leads to more accurate segmentation than traditional motion estimation that does not use physical constraints. Our method is suited to interactive segmentation, prominently used in commercial applications for cardiac analysis, where segmentation propagation is used to predict a segmentation in the next frame. We show that our method leads to more accurate predictions than a popular and recent interactive method used in cardiac segmentation.
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Zhao Q, Okada K, Rosenbaum K, Kehoe L, Zand DJ, Sze R, Summar M, Linguraru MG. Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA. Med Image Anal 2014; 18:699-710. [DOI: 10.1016/j.media.2014.04.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Revised: 03/25/2014] [Accepted: 04/02/2014] [Indexed: 01/06/2023]
Affiliation(s)
- Qian Zhao
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States.
| | - Kazunori Okada
- Computer Science Department, San Francisco State University, San Francisco, CA, United States
| | - Kenneth Rosenbaum
- Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States
| | - Lindsay Kehoe
- Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States
| | - Dina J Zand
- Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States
| | - Raymond Sze
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States; Department of Radiology, Children's National Medical Center, Washington, DC, United States
| | - Marshall Summar
- Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States; School of Medicine and Health Sciences, George Washington University, Washington, DC, United States
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Lim CW, Su Y, Yeo SY, Ng GM, Nguyen VT, Zhong L, Tan RS, Poh KK, Chai P. Automatic 4D reconstruction of patient-specific cardiac mesh with 1-to-1 vertex correspondence from segmented contours lines. PLoS One 2014; 9:e93747. [PMID: 24743555 PMCID: PMC3990569 DOI: 10.1371/journal.pone.0093747] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2012] [Accepted: 03/07/2014] [Indexed: 11/18/2022] Open
Abstract
We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial-temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.
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Affiliation(s)
- Chi Wan Lim
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
| | - Yi Su
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
| | - Si Yong Yeo
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
| | - Gillian Maria Ng
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
| | - Vinh Tan Nguyen
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
| | | | - Ru San Tan
- National Heart Centre Singapore, Singapore
| | - Kian Keong Poh
- Cardiac Department, National University Heart Center, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ping Chai
- Cardiac Department, National University Heart Center, National University Health System, Singapore, Singapore
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Mahapatra D. Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. J Digit Imaging 2014; 26:721-30. [PMID: 23319109 DOI: 10.1007/s10278-012-9548-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology (ETH), CAB F 61.1, Universitätstrasse 6, 8092 Zurich, Switzerland.
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Ringenberg J, Deo M, Devabhaktuni V, Berenfeld O, Snyder B, Boyers P, Gold J. Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:483-493. [PMID: 24345413 DOI: 10.1016/j.cmpb.2013.11.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 09/04/2013] [Accepted: 11/24/2013] [Indexed: 06/03/2023]
Abstract
We present a comprehensive validation analysis to assess the geometric impact of using coarsely-sliced short-axis images to reconstruct patient-specific cardiac geometry. The methods utilize high-resolution diffusion tensor MRI (DTMRI) datasets as reference geometries from which synthesized coarsely-sliced datasets simulating in vivo MRI were produced. 3D models are reconstructed from the coarse data using variational implicit surfaces through a commonly used modeling tool, CardioViz3D. The resulting geometries were then compared to the reference DTMRI models from which they were derived to analyze how well the synthesized geometries approximate the reference anatomy. Averaged over seven hearts, 95% spatial overlap, less than 3% volume variability, and normal-to-surface distance of 0.32 mm was observed between the synthesized myocardial geometries reconstructed from 8 mm sliced images and the reference data. The results provide strong supportive evidence to validate the hypothesis that coarsely-sliced MRI may be used to accurately reconstruct geometric ventricular models. Furthermore, the use of DTMRI for validation of in vivo MRI presents a novel benchmark procedure for studies which aim to substantiate their modeling and simulation methods using coarsely-sliced cardiac data. In addition, the paper outlines a suggested original procedure for deriving image-based ventricular models using the CardioViz3D software.
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Affiliation(s)
- Jordan Ringenberg
- EECS Department, College of Engineering, University of Toledo, 2801 West Bancroft Street, Toledo, OH 43606, United States.
| | - Makarand Deo
- Department of Engineering, Norfolk State University, 700 Park Avenue, Norfolk, VA 23504, United States
| | - Vijay Devabhaktuni
- EECS Department, College of Engineering, University of Toledo, 2801 West Bancroft Street, Toledo, OH 43606, United States
| | - Omer Berenfeld
- Center for Arrhythmia Research, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Brett Snyder
- EECS Department, College of Engineering, University of Toledo, 2801 West Bancroft Street, Toledo, OH 43606, United States
| | - Pamela Boyers
- Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States
| | - Jeffrey Gold
- Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States
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Ringenberg J, Deo M, Devabhaktuni V, Berenfeld O, Boyers P, Gold J. Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput Med Imaging Graph 2014; 38:190-201. [PMID: 24456907 DOI: 10.1016/j.compmedimag.2013.12.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 12/13/2013] [Accepted: 12/16/2013] [Indexed: 10/25/2022]
Abstract
This paper presents a fully automatic method to segment the right ventricle (RV) from short-axis cardiac MRI. A combination of a novel window-constrained accumulator thresholding technique, binary difference of Gaussian (DoG) filters, optimal thresholding, and morphology are utilized to drive the segmentation. A priori segmentation window constraints are incorporated to guide and refine the process, as well as to ensure appropriate area confinement of the segmentation. Training and testing were performed using a combined 48 patient datasets supplied by the organizers of the MICCAI 2012 right ventricle segmentation challenge, allowing for unbiased evaluations and benchmark comparisons. Marked improvements in speed and accuracy over the top existing methods are demonstrated.
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Affiliation(s)
- Jordan Ringenberg
- EECS Department, College of Engineering, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606, United States.
| | - Makarand Deo
- Department of Engineering, Norfolk State University, 700 Park Avenue, Norfolk, VA 23504, United States
| | - Vijay Devabhaktuni
- EECS Department, College of Engineering, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606, United States
| | - Omer Berenfeld
- Center for Arrhythmia Research, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Pamela Boyers
- Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States
| | - Jeffrey Gold
- Interprofessional Immersive Simulation Center, University of Toledo, 3000 Arlington Avenue, Toledo, OH 43614, United States
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Mahapatra D. Cardiac MRI segmentation using mutual context information from left and right ventricle. J Digit Imaging 2013; 26:898-908. [PMID: 23354341 PMCID: PMC3782609 DOI: 10.1007/s10278-013-9573-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty" that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1 Universitätstrasse, 68092, Zurich, Switzerland,
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Zhuang X. Challenges and Methodologies of Fully Automatic Whole Heart Segmentation: A Review. JOURNAL OF HEALTHCARE ENGINEERING 2013; 4:371-408. [DOI: 10.1260/2040-2295.4.3.371] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Lu YL, Connelly KA, Dick AJ, Wright GA, Radau PE. Automatic functional analysis of left ventricle in cardiac cine MRI. Quant Imaging Med Surg 2013; 3:200-9. [PMID: 24040616 PMCID: PMC3759139 DOI: 10.3978/j.issn.2223-4292.2013.08.02] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 08/02/2013] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37). MATERIALS AND METHODS The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively. RESULTS There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively). CONCLUSIONS The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.
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Affiliation(s)
- Ying-Li Lu
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Kim A. Connelly
- Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael’s Hospital and University of Toronto, Toronto, ON, Canada
- Cardiology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Alexander J. Dick
- Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Graham A. Wright
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, ON, Canada
| | - Perry E. Radau
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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Hu H, Liu H, Gao Z, Huang L. Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming. Magn Reson Imaging 2013; 31:575-84. [PMID: 23245907 DOI: 10.1016/j.mri.2012.10.004] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 09/10/2012] [Accepted: 10/14/2012] [Indexed: 11/25/2022]
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Punithakumar K, Noga M, Boulanger P. Cardiac right ventricular segmentation via point correspondence. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:4010-4013. [PMID: 24110611 DOI: 10.1109/embc.2013.6610424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This study presents an approach to the segmentation of the right ventricle (RV) from a sequence of cardiac magnetic resonance (MR) images. Automatic delineation of the RV is difficult because of its complex morphology, thin and ill-defined borders, and the photometric similarities between the connected cardiac regions such as papillary muscles and heart wall. Further, geometric/photometric models are hard to build from a finite training set because of the significant differences in size, shape, and intensity between subjects. In this study, we propose to use a non-rigid registration method developed recently to obtain the point correspondence in a sequence of cine MR images. Given the segmentation on the first frame, the proposed method segments both endocardial and epicardial borders of the RV using the obtained point correspondence, and relaxes the need of a training set. The proposed method is evaluated quantitatively on common data set by comparison with manual segmentation, which demonstrated competitive results in comparison with recent methods.
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Faghih Roohi S, Aghaeizadeh Zoroofi R. 4D statistical shape modeling of the left ventricle in cardiac MR images. Int J Comput Assist Radiol Surg 2012; 8:335-51. [PMID: 22893114 DOI: 10.1007/s11548-012-0787-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2012] [Accepted: 07/16/2012] [Indexed: 10/28/2022]
Abstract
PURPOSE Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifacts or partial volume effect. In this paper, we construct a 4D statistical model of the left ventricle using human cardiac short-axis MR images. METHODS Kernel PCA is utilized to explore the nonlinear variation of a population. The distribution of the landmarks is divided into the inter- and intra-subject subspaces. We compare the result of Kernel PCA with linear PCA and ICA for each of these subspaces. The initial atlas in natural coordinate system is built for the end-diastolic frame. The landmarks extracted from it are propagated to all frames of all datasets. We apply the 4D KPCA-based ASM for segmentation of all phases of a cardiac cycle and compare it with the conventional ASM. RESULTS The proposed statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. We investigate the behavior of the nonlinear model for different values of the kernel parameter. The results show that the model built by KPCA is less compact than PCA but more compact than ICA. Although for a constant number of modes the reconstruction error is a little higher for the KPCA-based statistical model, it produces a statistical model with substantially better specificity than PCA- and ICA-based models. CONCLUSION Quantitative analysis of the results demonstrates that our method improves the segmentation accuracy.
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Bhat S, Ohn J, Liebling M. Motion-based structure separation for label-free, high-speed, 3D cardiac microscopy. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:3638-3647. [PMID: 22531765 DOI: 10.1109/tip.2012.2195070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Capturing the dynamics of individual structures in the embryonic heart is an essential step for studying its function and development. Label-free brightfield (BF) microscopy allows for higher acquisition frame-rates than techniques requiring molecular labeling, without interfering with embryo viability or needing complex equipment. However, since different structures contribute similarly to image contrast, label-free microscopy lacks specificity. Here we mitigate this problem by separating a single-channel image series into multiple channels specific to different cardio-vascular structures, based only on their motion patterns. The technique combines images from multiple cardiac cycles and z-sections after non-uniform temporal registration to produce 3D+time image volumes of one full cardiac cycle with separate channels for static, transient and periodically moving structures. The resulting data is well-suited for velocity analysis and 3D-visualization. We characterize the separating capabilities of our technique on a synthetic cardiac dataset and demonstrate its practical applicability, by reconstructing three-channel views of the beating embryonic zebrafish heart with an effective frame rate of 1000 volumes (256×256×20 voxels each) per second. This technique enables quantitative characterization of dynamic heart function during cardiogenesis.
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50
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Tsai IC, Huang YL, Liu PT, Chen MC. Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography. Int J Comput Assist Radiol Surg 2012; 7:737-51. [PMID: 22528059 DOI: 10.1007/s11548-012-0688-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Accepted: 03/30/2012] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume. MATERIALS AND METHODS The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging. This work picks the middle of mid-ventricular level image slices as the lead slice. The proposed method develops two contouring modes to sketch the myocardium contour on the lead slice. By establishing the obtained contours as the initial contours, the region-growing method is employed to identify the contour of the myocardial region for each slice. The convex-hull finding algorithm is then used to refine the extracted contour. Finally, the width properties of the myocardial region and the morphological operators are used to obtain the entire LV myocardial volume. RESULTS Twenty-seven healthy patients who had no symptoms of ischemic heart disease are examined to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results using computer simulation reveal that the proposed method reliably identifies contours similar to those obtained using manual sketching. CONCLUSION The proposed method provides robust contouring for the LV myocardium on delayed-phase MDCT. The potential role of this technique may substantially reduce the time required to sketch manually a precise contour with high stability.
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Affiliation(s)
- I-Chen Tsai
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
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