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Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. PATTERN RECOGNITION 2024; 151:110424. [PMID: 38559674 PMCID: PMC10976951 DOI: 10.1016/j.patcog.2024.110424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
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Affiliation(s)
- Hao Guan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Liu Z, Kainth K, Zhou A, Deyer TW, Fayad ZA, Greenspan H, Mei X. A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation. NMR IN BIOMEDICINE 2024:e5143. [PMID: 38523402 DOI: 10.1002/nbm.5143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/26/2024]
Abstract
Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels.
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Affiliation(s)
- Zelong Liu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Komal Kainth
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander Zhou
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Timothy W Deyer
- East River Medical Imaging, New York, New York, USA
- Department of Radiology, Cornell Medicine, New York, New York, USA
| | - Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hayit Greenspan
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Xueyan Mei
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Lecesne E, Simon A, Garreau M, Barone-Rochette G, Fouard C. Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107841. [PMID: 37865006 DOI: 10.1016/j.cmpb.2023.107841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/15/2023] [Accepted: 10/01/2023] [Indexed: 10/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic segmentation of myocardial infarction is of great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late Gadolinium Enhancement cardiac MRI (LGE-MRI) is commonly used in clinical practice to quantify MI, which is crucial for clinical diagnosis and treatment of cardiac diseases. However, the segmentation of infarcted tissue in LGE-MRI is highly challenging due to its high anisotropy and inhomogeneities. METHODS The innovative aspect of our work lies in the utilization of a probability map of the healthy myocardium to guide the localization of infarction, as well as the combination of 2D U-Net and U-Net transformers to achieve the final segmentation. Instead of employing a binary segmentation map, we propose using a probability map of the normal myocardium, obtained through a dedicated 2D U-Net. To leverage spatial information, we employ a U-Net transformers network where we incorporate the probability map into the original image as an additional input. Then, To address the limitations of U-Net in segmenting accurately the contours, we introduce an adapted loss function. RESULTS Our method has been evaluated on the 2020 MICCAI EMIDEC challenge dataset, yielding competitive results. Specifically, we achieved a Dice score of 92.94% for the myocardium and 92.36% for the infarction. These outcomes highlight the competitiveness of our approach. CONCLUSION In the case of the infarction class, our proposed method outperforms state-of-the-art techniques across all metrics evaluated in the challenge, establishing its superior performance in infarction segmentation. This study further reinforces the importance of integrating a contour loss into the segmentation process.
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Affiliation(s)
- Erwan Lecesne
- Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, 35000, France.
| | - Antoine Simon
- Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, 35000, France
| | | | - Gilles Barone-Rochette
- Clinic of Cardiology, Cardiovascular and Thoracic Department, University Hospital of Grenoble, Grenoble, 38000, France
| | - Céline Fouard
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, Grenoble, 38000, France
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Jathanna N, Strachan K, Erhayiem B, Kamaruddin H, Swoboda P, Auer D, Chen X, Jamil-Copley S. The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol. J Cardiovasc Magn Reson 2023; 25:69. [PMID: 38008732 PMCID: PMC10680206 DOI: 10.1186/s12968-023-00978-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/12/2023] [Indexed: 11/28/2023] Open
Abstract
INTRODUCTION Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images. METHODS NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and testing to improve the performance, applicability and adaptability of an AI biventricular scar segmentation model being developed by the authors and (2) develop a curated, disease-specific imaging database to support future research and collaborations and, (3) to explore associations in clinical outcome for future risk prediction modelling studies. CONCLUSION NotIs CMR will collect and curate disease-specific, paired imaging and clinical datasets to develop an AI biventricular scar model whilst providing a database to support future research and collaboration in Artificial Intelligence and ischaemic heart disease.
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Affiliation(s)
- Nikesh Jathanna
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Kevin Strachan
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Bara Erhayiem
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Hazlyna Kamaruddin
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Peter Swoboda
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Dorothee Auer
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Xin Chen
- Department of Computer Science, University of Nottingham, Nottingham, UK
| | - Shahnaz Jamil-Copley
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK.
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Gao S, Zhou H, Gao Y, Zhuang X. BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med Image Anal 2023; 89:102889. [PMID: 37467643 DOI: 10.1016/j.media.2023.102889] [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: 03/01/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/21/2023]
Abstract
Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code is released via https://zmiclab.github.io/projects.html.
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Affiliation(s)
- Shangqi Gao
- School of Data Science, Fudan University, Shanghai, 200433, China
| | - Hangqi Zhou
- School of Data Science, Fudan University, Shanghai, 200433, China
| | - Yibo Gao
- School of Data Science, Fudan University, Shanghai, 200433, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, 200433, China. https://www.sdspeople.fudan.edu.cn/zhuangxiahai/
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Li L, Wu F, Wang S, Luo X, Martín-Isla C, Zhai S, Zhang J, Liu Y, Zhang Z, Ankenbrand MJ, Jiang H, Zhang X, Wang L, Arega TW, Altunok E, Zhao Z, Li F, Ma J, Yang X, Puybareau E, Oksuz I, Bricq S, Li W, Punithakumar K, Tsaftaris SA, Schreiber LM, Yang M, Liu G, Xia Y, Wang G, Escalera S, Zhuang X. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images. Med Image Anal 2023; 87:102808. [PMID: 37087838 DOI: 10.1016/j.media.2023.102808] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 01/11/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).
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Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Fuping Wu
- School of Data Science, Fudan University, Shanghai, China.
| | - Sihan Wang
- School of Data Science, Fudan University, Shanghai, China.
| | - Xinzhe Luo
- School of Data Science, Fudan University, Shanghai, China
| | - Carlos Martín-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Shuwei Zhai
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianpeng Zhang
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Yanfei Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
| | - Zhen Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Markus J Ankenbrand
- Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center, Wuerzburg University Hospitals, Wuerzburg, Germany
| | - Haochuan Jiang
- School of Engineering, University of Edinburgh, Edinburgh, UK; School of Robotics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Xiaoran Zhang
- Department of Electrical and Computer Engineering, University of California, LA, USA
| | - Linhong Wang
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | | | - Elif Altunok
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Zhou Zhao
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Feiyan Li
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jun Ma
- Department of Mathematics, Nanjing University of Science and Technology, Nanjing, China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, China
| | - Elodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Ilkay Oksuz
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Stephanie Bricq
- ImViA Laboratory, Université Bourgogne Franche-Comté, Dijon, France
| | - Weisheng Li
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | | | | | - Laura M Schreiber
- Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center, Wuerzburg University Hospitals, Wuerzburg, Germany
| | - Mingjing Yang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Guocai Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, China; National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, China
| | - Yong Xia
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Sergio Escalera
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain; Computer Vision Center, Universitat Autònoma de Barcelona, Spain
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders. Comput Med Imaging Graph 2023; 104:102158. [PMID: 36638626 DOI: 10.1016/j.compmedimag.2022.102158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.
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Jiang Z, He Y, Ye S, Shao P, Zhu X, Xu Y, Chen Y, Coatrieux JL, Li S, Yang G. O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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9
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Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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10
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Rahman SSMM, Chen Z, Lalande A, Decourselle T, Cochet A, Pommier T, Cottin Y, Salomon M, Couturier R. Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response. PLoS One 2023; 18:e0285165. [PMID: 37146017 PMCID: PMC10162556 DOI: 10.1371/journal.pone.0285165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 04/17/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND In acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis. OBJECTIVES This work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis. METHODS Machine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model's training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction). RESULTS The stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography. CONCLUSION Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.
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Affiliation(s)
| | - Zhihao Chen
- FEMTO-ST Institute, CNRS UMR 6174, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Alain Lalande
- ICMUB Laboratory, CNRS UMR 6302, Faculté de Médecine, Univ. Bourgogne Franche-Comté, Dijon, France
- Dijon Bourgogne University Hospital, Dijon, France
| | | | - Alexandre Cochet
- ICMUB Laboratory, CNRS UMR 6302, Faculté de Médecine, Univ. Bourgogne Franche-Comté, Dijon, France
- Dijon Bourgogne University Hospital, Dijon, France
| | | | - Yves Cottin
- Dijon Bourgogne University Hospital, Dijon, France
| | - Michel Salomon
- FEMTO-ST Institute, CNRS UMR 6174, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Raphaël Couturier
- FEMTO-ST Institute, CNRS UMR 6174, Univ. Bourgogne Franche-Comté, Belfort, France
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11
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Lustermans DRPRM, Amirrajab S, Veta M, Breeuwer M, Scannell CM. Optimized automated cardiac MR scar quantification with GAN-based data augmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107116. [PMID: 36148718 DOI: 10.1016/j.cmpb.2022.107116] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 08/26/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tested the hypothesis that a cascaded deep learning pipeline trained with augmentation by synthetically generated data would improve model accuracy and robustness for automated scar quantification. METHODS A cascaded pipeline consisting of three consecutive neural networks is proposed, starting with a bounding box regression network to identify a region of interest around the left ventricular (LV) myocardium. Two further nnU-Net models are then used to segment the myocardium and, if present, scar. The models were trained on the data from the EMIDEC challenge, supplemented with an extensive synthetic dataset generated with a conditional GAN. RESULTS The cascaded pipeline significantly outperformed a single nnU-Net directly segmenting both the myocardium (mean Dice similarity coefficient (DSC) (standard deviation (SD)): 0.84 (0.09) vs 0.63 (0.20), p < 0.01) and scar (DSC: 0.72 (0.34) vs 0.46 (0.39), p < 0.01) on a per-slice level. The inclusion of the synthetic data as data augmentation during training improved the scar segmentation DSC by 0.06 (p < 0.01). The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0.86 (0.03) and 0.67 (0.29) for myocardium and scar, respectively. CONCLUSION A cascaded deep learning-based pipeline trained with augmentation by synthetically generated data leads to myocardium and scar segmentations that are similar to the manual operator, and outperforms direct segmentation without the synthetic images.
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Affiliation(s)
- Didier R P R M Lustermans
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Sina Amirrajab
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of MR R&D - Clinical Science, Philips Healthcare, Best, the Netherlands
| | - Cian M Scannell
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
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12
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Farea Shaaf Z, Mahadi Abdul Jamil M, Ambar R, Abd Wahab MH. Convolutional Neural Network for Denoising Left Ventricle Magnetic Resonance Images. COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING APPROACHES IN BIOMEDICAL ENGINEERING AND HEALTH CARE SYSTEMS 2022:1-14. [DOI: 10.2174/9781681089553122010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Medical image processing is critical in disease detection and prediction. For
example, they locate lesions and measure an organ's morphological structures.
Currently, cardiac magnetic resonance imaging (CMRI) plays an essential role in
cardiac motion tracking and analyzing regional and global heart functions with high
accuracy and reproducibility. Cardiac MRI datasets are images taken during the heart's
cardiac cycles. These datasets require expert labeling to accurately recognize features
and train neural networks to predict cardiac disease. Any erroneous prediction caused
by image impairment will impact patients' diagnostic decisions. As a result, image
preprocessing is used, including enhancement tools such as filtering and denoising.
This paper introduces a denoising algorithm that uses a convolution neural network
(CNN) to delineate left ventricle (LV) contours (endocardium and epicardium borders)
from MRI images. With only a small amount of training data from the EMIDEC
database, this network performs well for MRI image denoising.
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Affiliation(s)
- Zakarya Farea Shaaf
- Universiti Tun Hussein Onn Malaysia,Biomedical Engineering Modelling and Simulation Research Group, Department Of Electronic Engineering, Faculty of Electrical And Electronic Engineering,,Johor,Malaysia
| | - Muhammad Mahadi Abdul Jamil
- Biomedical Engineering Modelling and Simulation Research Group, Department Of Electronic Engineering, Faculty of Electrical And Electronic Engineering,Universiti Tun Hussein Onn Malaysia,Johor,Malaysia
| | - Radzi Ambar
- Universiti Tun Hussein Onn Malaysia,Biomedical Engineering Modelling and Simulation Research Group, Department Of Electronic Engineering, Faculty of Electrical And Electronic Engineering,Johor,Malaysia
| | - Mohd Helmy Abd Wahab
- Universiti Tun Hussein Onn Malaysia,Biomedical Engineering Modelling and Simulation Research Group, Department Of Electronic Engineering, Faculty of Electrical And Electronic Engineering,Johor,Malaysia,86400
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13
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Sendra-Balcells C, Campello VM, Martín-Isla C, Viladés D, Descalzo ML, Guala A, Rodríguez-Palomares JF, Lekadir K. Domain generalization in deep learning for contrast-enhanced imaging. Comput Biol Med 2022; 149:106052. [DOI: 10.1016/j.compbiomed.2022.106052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/09/2022] [Accepted: 08/20/2022] [Indexed: 11/03/2022]
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14
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Chen Z, Shi J, Pommier T, Cottin Y, Salomon M, Decourselle T, Lalande A, Couturier R. Prediction of Myocardial Infarction From Patient Features With Machine Learning. Front Cardiovasc Med 2022; 9:754609. [PMID: 35369326 PMCID: PMC8964399 DOI: 10.3389/fcvm.2022.754609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 02/02/2022] [Indexed: 11/24/2022] Open
Abstract
This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.
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Affiliation(s)
- Zhihao Chen
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Jixi Shi
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
- IRSEEM, EA 4353, ESIGELEC, Univ. Normandie, Saint-Étienne-du-Rouvray, France
| | - Thibaut Pommier
- Department of Cardiology, University Hospital of Dijon, Dijon, France
| | - Yves Cottin
- Department of Cardiology, University Hospital of Dijon, Dijon, France
| | - Michel Salomon
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | | | - Alain Lalande
- Department of Medical Imaging, University Hospital of Dijon, Dijon, France
- ImViA Laboratory, EA 7535, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Raphaël Couturier
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
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15
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Huellebrand M, Ivantsits M, Tautz L, Kelle S, Hennemuth A. A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification. Front Cardiovasc Med 2022; 9:829512. [PMID: 35360025 PMCID: PMC8960112 DOI: 10.3389/fcvm.2022.829512] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 02/07/2022] [Indexed: 01/22/2023] Open
Abstract
The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes.
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16
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Brahim K, Arega TW, Boucher A, Bricq S, Sakly A, Meriaudeau F. An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net). SENSORS (BASEL, SWITZERLAND) 2022; 22:2084. [PMID: 35336258 PMCID: PMC8954140 DOI: 10.3390/s22062084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/18/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classification of prior information U-Net (ICPIU-Net) to efficiently segment the left ventricle (LV) myocardium, myocardial infarction (MI), and microvascular-obstructed (MVO) tissues from late gadolinium enhancement magnetic resonance (LGE-MR) images. Our approach was developed using two subnets cascaded to first segment the LV cavity and myocardium. Then, we used inclusion and classification constraint networks to improve the resulting segmentation of the diseased regions within the pre-segmented LV myocardium. This network incorporates the inclusion and classification information of the LGE-MRI to maintain topological constraints of pathological areas. In the testing stage, the outputs of each segmentation network obtained with specific estimated parameters from training were fused using the majority voting technique for the final label prediction of each voxel in the LGE-MR image. The proposed method was validated by comparing its results to manual drawings by experts from 50 LGE-MR images. Importantly, compared to various deep learning-based methods participating in the EMIDEC challenge, the results of our approach have a more significant agreement with manual contouring in segmenting myocardial diseases.
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Affiliation(s)
- Khawla Brahim
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
- National Engineering School of Sousse, University of Sousse, Sousse 4054, Tunisia
- LASEE Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia;
| | | | - Arnaud Boucher
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
| | - Stephanie Bricq
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
| | - Anis Sakly
- LASEE Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia;
| | - Fabrice Meriaudeau
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
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17
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Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Med Image Anal 2022; 79:102428. [DOI: 10.1016/j.media.2022.102428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 03/02/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022]
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18
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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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19
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Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12020414. [PMID: 35204504 PMCID: PMC8871002 DOI: 10.3390/diagnostics12020414] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/06/2022] [Accepted: 01/16/2022] [Indexed: 11/26/2022] Open
Abstract
Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.
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20
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Jabbar S, Bukhari ST, Mohy-ud-Din H. Multi-view SA-LA Net: A Framework for Simultaneous Segmentation of RV on Multi-view Cardiac MR Images. LECTURE NOTES IN COMPUTER SCIENCE 2022:277-286. [DOI: 10.1007/978-3-030-93722-5_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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21
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A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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22
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Chen Z, Lalande A, Salomon M, Decourselle T, Pommier T, Qayyum A, Shi J, Perrot G, Couturier R. Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI. Comput Med Imaging Graph 2021; 95:102014. [PMID: 34864579 DOI: 10.1016/j.compmedimag.2021.102014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 10/04/2021] [Accepted: 11/04/2021] [Indexed: 10/19/2022]
Abstract
Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this paper we propose an automatic myocardial infarction segmentation approach on the left ventricle from short-axis DE-MRI based on Convolutional Neural Networks (CNN). The objective is to segment myocardial infarction on short-axis DE-MRI images of the left ventricle acquired 10 min after the injection of a gadolinium-based contrast agent. The segmentation of the infarction area is realized in two stages: a first CNN model finds the contour of myocardium and a second CNN model segments the infarction. Compared to the manual intra-observer and inter-observer variations for the segmentation of myocardial infarction, and to the automatic segmentation with Gaussian Mixture Model, our proposal achieves satisfying segmentation results on our dataset of 904 DE-MRI slices.
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Affiliation(s)
- Zhihao Chen
- FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Alain Lalande
- ImViA Laboratory, EA7535, Univ. Bourgogne Franche-Comté, Dijon, France; Department of Medical Imaging, University Hospital of Dijon, Dijon, France
| | - Michel Salomon
- FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | | | - Thibaut Pommier
- Department of Cardiology, University Hospital of Dijon, Dijon, France
| | - Abdul Qayyum
- ImViA Laboratory, EA7535, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Jixi Shi
- FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France; IRSEEM, EA4353, ESIGELEC, Univ. Normandie, Rouen, France
| | - Gilles Perrot
- FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Raphaël Couturier
- FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France.
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23
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Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks. ALGORITHMS 2021. [DOI: 10.3390/a14080249] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.
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