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Zhong P, Cheng R, Tang X. Utilizing average symmetrical surface distance in active shape modeling for subcortical surface generation with slow-fast learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:230-233. [PMID: 36086301 DOI: 10.1109/embc48229.2022.9871829] [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
In this paper, we propose and validate an automatic pipeline for subcortical surface generation by making use of the average symmetrical surface distance (ASSD) loss in active shape modeling (ASM). A group of template surfaces are first generated via large deformation diffeomorphic metric mapping based surface deformation. ASM is then employed to obtain the mean shape and shape variation parameters of the template surfaces. To obtain the optimal shape variation parameters which best fit the target structure after acting upon the mean shape, a recently proposed derivative-free optimization method (the slow-fast learning method) is adopted. The ASSD loss, in addition to the typically utilized Dice similarity coefficient loss, is employed during the learning process to help enhance the boundary accuracy. We successfully validate the importance of the ASSD loss through ablation analysis. In addition, we show the effectiveness of the slow-fast learning method by comparing it with other state-of-the-art derivative-free optimization algorithms. Our proposed pipeline is found to be capable of yielding subcortical surfaces with high accuracy, correct anatomical topology, and sufficient smoothness. Clinical Relevance- This work provides a useful tool for generating subcortical surfaces which are important biomarkers for a variety of brain disorders.
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Li L, Zimmer VA, Schnabel JA, Zhuang X. Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review. Med Image Anal 2022; 77:102360. [PMID: 35124370 PMCID: PMC7614005 DOI: 10.1016/j.media.2022.102360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/04/2021] [Accepted: 01/10/2022] [Indexed: 02/08/2023]
Abstract
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.
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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; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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Li L, Zimmer VA, Schnabel JA, Zhuang X. AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med Image Anal 2022; 76:102303. [PMID: 34875581 DOI: 10.1016/j.media.2021.102303] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/08/2021] [Accepted: 11/08/2021] [Indexed: 10/19/2022]
Abstract
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 post-ablation LGE MRIs from the MICCAI2018 Atrial Segmentation Challenge. Moreover, we explored the domain generalization ability of the proposed AtrialJSQnet on 40 pre-ablation LGE MRIs from this challenge and 30 post-ablation multi-center LGE MRIs from another challenge (ISBI2012 Left Atrium Fibrosis and Scar Segmentation Challenge). Extensive experiments on public datasets demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code has been released via https://zmiclab.github.io/projects.html.
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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; School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK; Technical University Munich, Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK; Technical University Munich, Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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Wu Y, Tang Z, Li B, Firmin D, Yang G. Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives. Front Physiol 2021; 12:709230. [PMID: 34413789 PMCID: PMC8369509 DOI: 10.3389/fphys.2021.709230] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/28/2021] [Indexed: 12/03/2022] Open
Abstract
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
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Affiliation(s)
- Yinzhe Wu
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Binghuan Li
- Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - David Firmin
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
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Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, Vesal S, Ravikumar N, Maier A, Yang X, Heng PA, Ni D, Li C, Tong Q, Si W, Puybareau E, Khoudli Y, Géraud T, Chen C, Bai W, Rueckert D, Xu L, Zhuang X, Luo X, Jia S, Sermesant M, Liu Y, Wang K, Borra D, Masci A, Corsi C, de Vente C, Veta M, Karim R, Preetha CJ, Engelhardt S, Qiao M, Wang Y, Tao Q, Nuñez-Garcia M, Camara O, Savioli N, Lamata P, Zhao J. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med Image Anal 2021; 67:101832. [PMID: 33166776 DOI: 10.1016/j.media.2020.101832] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 12/29/2022]
Abstract
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.
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Affiliation(s)
- Zhaohan Xiong
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Qing Xia
- State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Zhiqiang Hu
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | | | - Cheng Bian
- Tencent Jarvis Laboratory, Shenzhen, China
| | | | - Sulaiman Vesal
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nishant Ravikumar
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Yang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Caizi Li
- School of Computer Science, Wuhan University, Wuhan, China
| | - Qianqian Tong
- School of Computer Science, Wuhan University, Wuhan, China
| | - Weixin Si
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Younes Khoudli
- EPITA Research and Development Laboratory, Paris, France
| | - Thierry Géraud
- EPITA Research and Development Laboratory, Paris, France
| | - Chen Chen
- Department of Computing, Imperial College London, London, United Kingdom
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Lingchao Xu
- School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Xinzhe Luo
- School of Data Science, Fudan University, Shanghai, China
| | - Shuman Jia
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Maxime Sermesant
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Yashu Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Davide Borra
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Alessandro Masci
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Cristiana Corsi
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Coen de Vente
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Rashed Karim
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom
| | | | - Sandy Engelhardt
- Department of Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | - Menyun Qiao
- Biomedical Engineering Center, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- Biomedical Engineering Center, Fudan University, Shanghai, China
| | - Qian Tao
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marta Nuñez-Garcia
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Oscar Camara
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nicolo Savioli
- Department of Bioengineering, Kings College London, London, United Kingdom
| | - Pablo Lamata
- Department of Bioengineering, Kings College London, London, United Kingdom
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
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Agrawal P, Whitaker RT, Elhabian SY. An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2316-2326. [PMID: 31985415 PMCID: PMC7395849 DOI: 10.1109/tmi.2020.2968917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Multi-label probabilistic maps, a.k.a. probabilistic segmentations, parameterize a population of intimately co-existing anatomical shapes and are useful for various medical imaging applications, such as segmentation, anatomical atlases, shape analysis, and consensus generation. Existing methods to estimate probabilistic segmentations rely on ad hoc intermediate representations (e.g., average of Gaussian-smoothed label maps and smoothed signed distance maps) that do not necessarily conform to the underlying generative process. Generative modeling of such maps could help discover as well as aide in the statistical analysis of sub-groups in a population via clustering and mixture modeling techniques. In this paper, we propose an estimation of multi-label probabilistic maps and showcase their favorable performance for modeling anatomical shapes such as the left atrium of the human heart and brain structures. The proposed formulation relies on a constrained optimization in the natural parameter space of the exponential family form of categorical distributions. A smoothness prior provides generalizability in the model and helps achieve greater performance in modeling tasks for unseen samples. We demonstrate and compare the effectiveness of the proposed method for Bayesian image segmentation, multi-atlas segmentation, and shape-based clustering.
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Li L, Wu F, Yang G, Xu L, Wong T, Mohiaddin R, Firmin D, Keegan J, Zhuang X. Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Med Image Anal 2019; 60:101595. [PMID: 31811981 PMCID: PMC6988106 DOI: 10.1016/j.media.2019.101595] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Revised: 06/05/2019] [Accepted: 10/26/2019] [Indexed: 11/06/2022]
Abstract
Propose a fully automatic method for left atrial scar quantification, with promising performance. Formulate a new framework of scar quantification based on surface projection and graph-cuts framework. Propose the multi-scale learning CNN, combined with the random shift training strategy, to learn and predict the graph potentials, which significantly improves the performance of the proposed method, and enables the full automation of the framework. Provide thorough validation and parameter studies for the proposed techniques using fifty-eight clinical images.
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.
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Affiliation(s)
- Lei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Data Science, Fudan University, Shanghai, China
| | - Fuping Wu
- School of Data Science, Fudan University, Shanghai, China; Dept of Statistics, School of Management, Fudan University, Shanghai, China
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Lingchao Xu
- School of NAOCE, Shanghai Jiao Tong University, Shanghai, China
| | - Tom Wong
- Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Raad Mohiaddin
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - David Firmin
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Jennifer Keegan
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China; Fudan-Xinzailing Joint Research Center for Big Data, Fudan University, Shanghai, China.
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Xiong Z, Fedorov VV, Fu X, Cheng E, Macleod R, Zhao J. Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:515-524. [PMID: 30716023 PMCID: PMC6364320 DOI: 10.1109/tmi.2018.2866845] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm ×0.625 mm ×1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm3 of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.
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Affiliation(s)
- Zhaohan Xiong
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Vadim V. Fedorov
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Xiaohang Fu
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Elizabeth Cheng
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Rob Macleod
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Jichao Zhao
- VVF is with Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA. RM is with the Department of Bioengineering, University of Utah, Salt Lake City, USA
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