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Cho Y, Park S, Hwang SH, Ko M, Lim DS, Yu CW, Park SM, Kim MN, Oh YW, Yang G. Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography. J Korean Med Sci 2023; 38:e306. [PMID: 37724499 PMCID: PMC10506901 DOI: 10.3346/jkms.2023.38.e306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/03/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). METHODS This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). RESULTS In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. CONCLUSION Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.
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Affiliation(s)
- Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
- AI Center, Korea University Anam Hospital, Seoul, Korea
| | - Soojung Park
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
| | - Minseok Ko
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Do-Sun Lim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Cheol Woong Yu
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Seong-Mi Park
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Mi-Na Kim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Yu-Whan Oh
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- Bioengineering Department and Imperial-X, Imperial College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
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Uslu F. GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images. Comput Med Imaging Graph 2023; 108:102266. [PMID: 37385047 DOI: 10.1016/j.compmedimag.2023.102266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/04/2023] [Accepted: 06/11/2023] [Indexed: 07/01/2023]
Abstract
Atrial Fibrillation (AF) is a disease where the atria fail to properly contract but quiver instead, due to the abnormal electrical activity of the atrial tissue. In AF patients, anatomical and functional parameters of the left atrium (LA) largely differ from that of healthy people due to LA remodelling, which can continue in many cases after the catheter ablation treatment. Therefore, it is important to follow up with AF patients to detect any recurrence. LA segmentation masks obtained from short-axis CINE MRI images are used as the gold standard for the quantification of LA parameters. Thick slices of CINE MRI images hinder the use of 3D networks for segmentation while 2D architectures often fail to model inter-slice dependencies. This study presents GSM-Net which approximates 3D networks with effective modelling of inter-slice similarities with two new modules: global slice sequence encoder (GSSE) and sequence dependent channel attention module (SdCAt). In contrast to previous work modelling only local inter-slice similarities, GSSE also models global spatial dependencies across slices. SdCAt generates a distribution of attention weights over MRI slices per channel, to better trace characteristic changes in the size of the LA or other structures across slices. We found that GSM-Net outperforms previous methods on LA segmentation and helps to identify AF recurrence patients. We believe that GSM-Net can be used as an automatic tool to estimate LA parameters such as ejection fraction to identify AF, and to follow up with patients after treatment to detect any recurrence.
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Affiliation(s)
- Fatmatülzehra Uslu
- Bursa Technical University, Electrical and Electronics Engineering Department, Bursa, 16310, Türkiye.
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Cho Y, Cho H, Shim J, Choi JI, Kim YH, Kim N, Oh YW, Hwang SH. Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging. J Korean Med Sci 2022; 37:e271. [PMID: 36123960 PMCID: PMC9485068 DOI: 10.3346/jkms.2022.37.e271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/25/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). METHODS An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. RESULTS The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (-14.90-27.61), 6.21% (-9.62-22.03), and 2.68% (-8.57-13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. CONCLUSION Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.
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Affiliation(s)
- Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Seoul,Korea
- AI Center, Korea University Anam Hospital, Seoul,Korea
| | - Hyungjoon Cho
- Department of Radiology, Korea University Anam Hospital, Seoul,Korea
| | - Jaemin Shim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul,Korea
| | - Jong-Il Choi
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul,Korea
| | - Young-Hoon Kim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul,Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul,Korea.
| | - Yu-Whan Oh
- Department of Radiology, Korea University Anam Hospital, Seoul,Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Seoul,Korea.
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Grigoriadis GI, Zaridis D, Pezoulas VC, Nikopoulos S, Sakellarios AI, Tachos NS, Naka KK, Michalis LK, Fotiadis DI. Segmentation of left atrium using CT images and a deep learning model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3839-3842. [PMID: 36086640 DOI: 10.1109/embc48229.2022.9871623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The left atrium (LA) is one of the cardiac cavities with the most complex anatomical structures. Its role in the clinical diagnosis and patient's management is critical, as it is responsible for the atrial fibrillation, a condition that promotes the thrombogenesis inside the left atrial appendage. The development of an automated approach for LA segmentation is a demanding task mainly due to its anatomical complexity and the variation of its shape among patients. In this study, we focus to develop an unbiased pipeline capable to segment the atrial cavity from CT images. For evaluation purposes state-of-the-art metrics were used to assess the segmentation results. Particularly, the results indicated the mean values of the dice score 80%, the hausdorff distance 11.78mm, the average surface distance 2.24mm and the rand error index 0.2.
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Chen Y, Xie W, Zhang J, Qiu H, Zeng D, Shi Y, Yuan H, Zhuang J, Jia Q, Zhang Y, Dong Y, Huang M, Xu X. Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis. Front Cardiovasc Med 2022; 9:804442. [PMID: 35282363 PMCID: PMC8914019 DOI: 10.3389/fcvm.2022.804442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/07/2022] [Indexed: 11/13/2022] Open
Abstract
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.
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Affiliation(s)
- Yutian Chen
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
- Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Wen Xie
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
| | - Jiawei Zhang
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
| | - Hailong Qiu
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
| | - Dewen Zeng
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Yiyu Shi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Haiyun Yuan
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
| | - Jian Zhuang
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
| | - Qianjun Jia
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Catheterization Lab, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
| | - Yuhao Dong
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
- Department of Catheterization Lab, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Yuhao Dong
| | - Meiping Huang
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
- Department of Catheterization Lab, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Meiping Huang
| | - Xiaowei Xu
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China
- Xiaowei Xu
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Chen J, Zhang H, Mohiaddin R, Wong T, Firmin D, Keegan J, Yang G. Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:420-433. [PMID: 34534077 DOI: 10.1109/tmi.2021.3113678] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.
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7
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You C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan JS. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. LECTURE NOTES IN COMPUTER SCIENCE 2022:3-16. [PMCID: PMC10323962 DOI: 10.1007/978-3-031-18523-6_1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
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Peters DC, Lamy J, Sinusas AJ, Baldassarre LA. Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers. Eur Heart J Cardiovasc Imaging 2021; 23:14-30. [PMID: 34718484 DOI: 10.1093/ehjci/jeab221] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022] Open
Abstract
Left atrial (LA) imaging is still not routinely used for diagnosis and risk stratification, although recent studies have emphasized its importance as an imaging biomarker. Cardiovascular magnetic resonance is able to evaluate LA structure and function, metrics that serve as early indicators of disease, and provide prognostic information, e.g. regarding diastolic dysfunction, and atrial fibrillation (AF). MR angiography defines atrial anatomy, useful for planning ablation procedures, and also for characterizing atrial shapes and sizes that might predict cardiovascular events, e.g. stroke. Long-axis cine images can be evaluated to define minimum, maximum, and pre-atrial contraction LA volumes, and ejection fractions (EFs). More modern feature tracking of these cine images provides longitudinal LA strain through the cardiac cycle, and strain rates. Strain may be a more sensitive marker than EF and can predict post-operative AF, AF recurrence after ablation, outcomes in hypertrophic cardiomyopathy, stratification of diastolic dysfunction, and strain correlates with atrial fibrosis. Using high-resolution late gadolinium enhancement (LGE), the extent of fibrosis in the LA can be estimated and post-ablation scar can be evaluated. The LA LGE method is widely available, its reproducibility is good, and validations with voltage-mapping exist, although further scan-rescan studies are needed, and consensus regarding atrial segmentation is lacking. Using LGE, scar patterns after ablation in AF subjects can be reproducibly defined. Evaluation of 'pre-existent' atrial fibrosis may have roles in predicting AF recurrence after ablation, predicting new-onset AF and diastolic dysfunction in patients without AF. LA imaging biomarkers are ready to enter into diagnostic clinical practice.
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Affiliation(s)
- Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Cardiology, Yale School of Medicine, New Haven, CT, USA
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Gonzales RA, Seemann F, Lamy J, Arvidsson PM, Heiberg E, Murray V, Peters DC. Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours. BMC Med Imaging 2021; 21:101. [PMID: 34147081 PMCID: PMC8214286 DOI: 10.1186/s12880-021-00630-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/10/2021] [Indexed: 12/28/2022] Open
Abstract
Background Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. Methods This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. Results The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. Conclusion The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. Video Abstract
Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00630-3.
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Affiliation(s)
- Ricardo A Gonzales
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.,Department of Electrical Engineering, Universidad de Ingeniería y Tecnología, Lima, Peru.,Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Felicia Seemann
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.,Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden.,Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Per M Arvidsson
- Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Einar Heiberg
- Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden.,Department of Biomedical Engineering, Lund University, Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Victor Murray
- Department of Electrical Engineering, Universidad de Ingeniería y Tecnología, Lima, Peru.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.
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Jeon B, Jung S, Shim H, Chang HJ. Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images. ENTROPY (BASEL, SWITZERLAND) 2021; 23:E64. [PMID: 33401695 PMCID: PMC7824462 DOI: 10.3390/e23010064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/18/2020] [Accepted: 12/27/2020] [Indexed: 11/16/2022]
Abstract
We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.
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Affiliation(s)
- Byunghwan Jeon
- School of Computer Science, Kyungil University, Gyeongsan 38428, Korea;
| | - Sunghee Jung
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
| | - Hackjoon Shim
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
| | - Hyuk-Jae Chang
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
- Division of Cardiology Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
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