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Zhou Z, Wan H, Zhang H, Chen X, Wang X, Lili S, Zhang T. Segmentation of Spontaneous Intracerebral Hemorrhage on CT With a Region Growing Method Based on Watershed Preprocessing. Front Neurol 2022; 13:865023. [PMID: 35422751 PMCID: PMC9002175 DOI: 10.3389/fneur.2022.865023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Intracerebral hemorrhage (ICH) poses a great threat to human life due to its high incidence and poor prognosis. Identification of the bleeding location and quantification of the volume based on CT images are of great significance for assisting the diagnosis and treatment of ICH. In this study, a region-growing algorithm based on watershed preprocessing (RG-WP) was proposed to segment and quantify the hemorrhage. The lowest points yielded by the watershed algorithm were used as seed points for region growing and then hemorrhage was segmented based on the region growing method. At the same time, to integrate the rich experience of clinicians with the algorithm, manual selection of seed points on the basis of watershed segmentation was performed. With the application of segmentation on CT images of 55 patients with ICH, the performance of the RG-WP algorithm was evaluated by comparing it with manual segmentations delineated by professional clinicians as well as the traditional ABC/2 method and the deep learning algorithm U-net. The mean deviation of hemorrhage volume of the RG-WP algorithm from manual segmentation was −0.12 ml (range: −1.05–1.16), while that of the ABC/2 from the manual was 1.05 ml (range: −0.77–9.57). Strong agreement of the algorithm and the manual was confirmed with a high intraclass correlation coefficient (ICC) (0.998, 95% CI: 0.997–0.999), which was superior to that of the ABC/2 and the manual (0.972, 95% CI: 0.953–0.984). The sensitivity (Sen), positive predictive value (PPV), dice similarity index (DSI), and Jaccard index (JI) of the RG-WP algorithm compared to the manual were 0.92 ± 0.04, 0.95 ± 0.04, 0.93 ± 0.02, and 0.88 ± 0.04, respectively, showing high consistency. Besides, the accuracy of the algorithm was also comparable to that of the deep learning method U-net, with Sen, PPV, DSI, and JI being 0.91 ± 0.09, 0.91 ± 0.06, 0.91 ± 0.05, and 0.91 ± 0.06, respectively.
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Affiliation(s)
- Zhengsong Zhou
- Department of Electronic Information Engineering, Chengdu Jincheng College, Chengdu, China
| | - Hongli Wan
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Haoyu Zhang
- Department of Electronic Information Engineering, Chengdu Jincheng College, Chengdu, China
| | - Xumiao Chen
- Department of Electronic Information Engineering, Chengdu Jincheng College, Chengdu, China
| | - Xiaoyu Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Shiluo Lili
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tao Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- *Correspondence: Tao Zhang
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Mistral T, Roca P, Maggia C, Tucholka A, Forbes F, Doyle S, Krainik A, Galanaud D, Schmitt E, Kremer S, Kastler A, Troprès I, Barbier EL, Payen JF, Dojat M. Automated Quantification of Brain Lesion Volume From Post-trauma MR Diffusion-Weighted Images. Front Neurol 2022; 12:740603. [PMID: 35281992 PMCID: PMC8905597 DOI: 10.3389/fneur.2021.740603] [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: 07/13/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
ObjectivesDetermining the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming and cannot therefore be used in routine practice. The study aimed to evaluate the feasibility of an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images.MethodsThe performance of AQP was measured against manual delineation consensus by independent raters in two series of experiments based on: (i) realistic trauma phantoms (n = 5) where low and high MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; (ii) severe TBI patients (n = 12 patients) who underwent MR imaging within 10 days after injury.ResultsIn realistic TBI phantoms, no statistical differences in Dice similarity coefficient, precision and brain lesion volumes were found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19–84 ml) (median; 25–75th centiles).ConclusionsOur results support the feasibility of using an automated quantification procedure to determine, with similar accuracy to manual delineation, the volume of low and high MD brain lesions after trauma, and thus allow the determination of the type and volume of edematous brain lesions. This approach had comparable performance with manual delineation by a panel of experts. It will be tested in a large cohort of patients enrolled in the multicenter OxyTC trial (NCT02754063).
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Affiliation(s)
- Thomas Mistral
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
| | | | - Christophe Maggia
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
| | | | - Florence Forbes
- Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | | | - Alexandre Krainik
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, CNRS, IRMaGe, Grenoble, France
| | | | | | | | - Adrian Kastler
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
| | - Irène Troprès
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, CNRS, IRMaGe, Grenoble, France
| | - Emmanuel L. Barbier
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, CNRS, IRMaGe, Grenoble, France
| | - Jean-François Payen
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
| | - Michel Dojat
- Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France
- *Correspondence: Michel Dojat
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3
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Towards subject-level cerebral infarction classification of CT scans using convolutional networks. PLoS One 2020; 15:e0235765. [PMID: 32667947 PMCID: PMC7363075 DOI: 10.1371/journal.pone.0235765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 06/23/2020] [Indexed: 11/24/2022] Open
Abstract
Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed tomography scans on volume level for the presence of non-acute cerebral infarction. This is not a trivial task, as the lesions are often similar to other areas in the brain regarding shape and intensity. A three stage architecture is used for classification: 1) A cranial cavity segmentation network is developed, trained and applied. 2) Region proposals are generated 3) Connected regions are classified using a multi-resolution, densely connected 3D convolutional network. Mean area under curve values for subject level classification are 0.95 for the unstratified test set, 0.88 for stratification by patient age and 0.93 for stratification by CT scanner model. We use a partly segmented dataset of 555 scans of which 186 scans are used in the unstratified test set. Furthermore we examine possible dataset bias for scanner model and patient age parameters. We show a successful application of the proposed three-stage model for full volume classification. In contrast to black-box approaches, the convolutional network’s decision can be further assessed by examination of intermediate segmentation results.
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van de Leemput SC, Prokop M, van Ginneken B, Manniesing R. Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-Contrast CT From Spatiotemporal 4D CT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:985-996. [PMID: 31484111 DOI: 10.1109/tmi.2019.2939044] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perfusion (CTP) images. This results in reduced workup time and radiation dose. To achieve this, we present a stacked bidirectional convolutional LSTM (C-LSTM) network to predict 3D volumes from 4D spatiotemporal data. Several parameterizations of the C-LSTM network were trained on a set of 17 CTP-NCCT pairs to learn to derive a NCCT from CTP and were subsequently quantitatively evaluated on a separate cohort of 16 cases. The results show that the C-LSTM network clearly outperforms the baseline and competitive convolutional neural network methods. We show good scalability and performance of the method by continued training and testing on an independent dataset which includes pathology of 80 and 83 CTP-NCCT pairs, respectively. C-LSTM is, therefore, a promising general deep learning approach to learn from high-dimensional spatiotemporal medical images.
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Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019; 26:1695-1706. [PMID: 31405724 PMCID: PMC6878163 DOI: 10.1016/j.acra.2019.07.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. MATERIALS AND METHODS The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. RESULTS The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. CONCLUSION These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Laura Heacock
- Department of Radiology, NYU Langone, New York, New York
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania
| | - Jason Itri
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157
| | - Christopher G Filippi
- Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - James Lee
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | | | - Tara Retson
- Department of Radiology, University of California San Diego, San Diego, California
| | - Kendra Godwin
- Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joey Nicholson
- NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
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Patel A, Schreuder FHBM, Klijn CJM, Prokop M, Ginneken BV, Marquering HA, Roos YBWEM, Baharoglu MI, Meijer FJA, Manniesing R. Intracerebral Haemorrhage Segmentation in Non-Contrast CT. Sci Rep 2019; 9:17858. [PMID: 31780815 PMCID: PMC6882855 DOI: 10.1038/s41598-019-54491-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 11/11/2019] [Indexed: 12/01/2022] Open
Abstract
A 3-dimensional (3D) convolutional neural network is presented for the segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non-contrast computed tomography (NCCT). The method utilises a combination of contextual information on multiple scales for fast and fully automatic dense predictions. To handle a large class imbalance present in the data, a weight map is introduced during training. The method was evaluated on two datasets of 25 and 50 patients respectively. The reference standard consisted of manual annotations for each ICH in the dataset. Quantitative analysis showed a median Dice similarity coefficient of 0.91 [0.87-0.94] and 0.90 [0.85-0.92] for the two test datasets in comparison to the reference standards. Evaluation of a separate dataset of 5 patients for the assessment of the observer variability produced a mean Dice similarity coefficient of 0.95 ± 0.02 for the inter-observer variability and 0.97 ± 0.01 for the intra-observer variability. The average prediction time for an entire volume was 104 ± 15 seconds. The results demonstrate that the method is accurate and approaches the performance of expert manual annotation.
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Affiliation(s)
- Ajay Patel
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands.
| | - Floris H B M Schreuder
- Department of Neurology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Henk A Marquering
- Biomedical Engineering & Physics Department, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Physics, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Yvo B W E M Roos
- Department of Neurology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - M Irem Baharoglu
- Department of Neurology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Rashindra Manniesing
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
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Melingi SB, Vijayalakshmi V. Automatic segmentation of sub-acute ischemic stroke lesion by using DTCWT and DBN with parameter fine tuning. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00240-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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8
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Kim H, Son TG, Lee J, Kim HA, Cho H, Jeong WS, Choi JW, Kim Y. Three-dimensional orbital wall modeling using paranasal sinus segmentation. J Craniomaxillofac Surg 2019; 47:959-967. [PMID: 31027858 DOI: 10.1016/j.jcms.2019.03.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/11/2019] [Accepted: 03/25/2019] [Indexed: 10/27/2022] Open
Abstract
PURPOSE Three-dimensional orbital wall modeling is a time-consuming process because of the presence of pseudoforamina. We developed an automated three-dimensional modeling software to characterize the orbital wall, and evaluated it using data from fracture patients. METHODS We first characterized the air and face regions using multiphase segmentation; the sinuses were segmented by applying morphological operations to air regions. Pseudoforamina of the orbital wall were offset with the segmented sinuses. Finally, the three-dimensional facial bone model, with orbital wall, was reconstructed from the segmented images. RESULTS Ten computed tomography data sets were used to evaluate the proposed method. Results were compared with those obtained using the active contour model and manual segmentation. The process took 31.7 ± 8.0 s, which was 30-60 times faster than other methods. The average distances between surfaces obtained with the proposed method and those obtained with manual segmentation (normal side: 0.20 ± 0.06 mm; fractured side: 0.28 ± 0.10 mm) were approximately half those obtained using the active contour model. CONCLUSIONS Three-dimensional orbital wall models, which were very similar to the manually segmented models, were archived within 1 min using the developed software, regardless of fracture presence. The proposed method might improve the safety and accuracy of surgical procedures.
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Affiliation(s)
- Hannah Kim
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of Korea.
| | - Tae-Geun Son
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea.
| | - Hyeun A Kim
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea.
| | - Hyunchul Cho
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea.
| | - Woo Shik Jeong
- Department of Plastic and Reconstructive Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Jong Woo Choi
- Department of Plastic and Reconstructive Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea; Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea.
| | - Youngjun Kim
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of Korea.
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Gupta A, Mallick P, Sharma O, Gupta R, Duggal R. PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma. PLoS One 2018; 13:e0207908. [PMID: 30540767 PMCID: PMC6291116 DOI: 10.1371/journal.pone.0207908] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 11/08/2018] [Indexed: 02/03/2023] Open
Abstract
Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.
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Affiliation(s)
- Anubha Gupta
- SBILab, Department of ECE, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
- * E-mail:
| | - Pramit Mallick
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York City, New York, United States of America
| | - Ojaswa Sharma
- Department of CSE, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B. R.A. IRCH, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Rahul Duggal
- SBILab, Department of ECE, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
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10
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Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: A fuzzy approach. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Meijs M, Patel A, van de Leemput SC, Prokop M, van Dijk EJ, de Leeuw FE, Meijer FJA, van Ginneken B, Manniesing R. Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients. Sci Rep 2017; 7:15622. [PMID: 29142240 PMCID: PMC5688074 DOI: 10.1038/s41598-017-15617-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 10/31/2017] [Indexed: 11/13/2022] Open
Abstract
A robust method is presented for the segmentation of the full cerebral vasculature in 4-dimensional (4D) computed tomography (CT). The method consists of candidate vessel selection, feature extraction, random forest classification and postprocessing. Image features include among others the weighted temporal variance image and parameters, including entropy, of an intensity histogram in a local region at different scales. These histogram parameters revealed to be a strong feature in the detection of vessels regardless of shape and size. The method was trained and tested on a large database of 264 patients with suspicion of acute ischemia who underwent 4D CT in our hospital in the period January 2014 to December 2015. Five subvolumes representing different regions of the cerebral vasculature were annotated in each image in the training set by medical assistants. The evaluation was done on 242 patients. A total of 16 (<8%) patients showed severe under or over segmentation and were reported as failures. One out of five subvolumes was randomly annotated in 159 patients and was used for quantitative evaluation. Quantitative evaluation showed a Dice coefficient of 0.91 ± 0.07 and a modified Hausdorff distance of 0.23 ± 0.22 mm. Therefore, robust vessel segmentation in 4D CT is feasible with good accuracy.
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Affiliation(s)
- Midas Meijs
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands.
| | - Ajay Patel
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Sil C van de Leemput
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Ewoud J van Dijk
- Department of Neurology, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
| | - Rashindra Manniesing
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525GA, Nijmegen, The Netherlands
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