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Villegas-Martinez M, de Villedon de Naide V, Muthurangu V, Bustin A. The beating heart: artificial intelligence for cardiovascular application in the clinic. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01180-9. [PMID: 38907767 DOI: 10.1007/s10334-024-01180-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/25/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.
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Affiliation(s)
- Manuel Villegas-Martinez
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Victor de Villedon de Naide
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Vivek Muthurangu
- Center for Cardiovascular Imaging, UCL Institute of Cardiovascular Science, University College London, London, WC1N 1EH, UK
| | - Aurélien Bustin
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France.
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France.
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Kalapos A, Szabó L, Dohy Z, Kiss M, Merkely B, Gyires-Tóth B, Vágó H. Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning. Front Cardiovasc Med 2023; 10:1147581. [PMID: 37522085 PMCID: PMC10374405 DOI: 10.3389/fcvm.2023.1147581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Structural and functional heart abnormalities can be examined non-invasively with cardiac magnetic resonance imaging (CMR). Thanks to the development of MR devices, diagnostic scans can capture more and more relevant information about possible heart diseases. T1 and T2 mapping are such novel technology, providing tissue specific information even without the administration of contrast material. Artificial intelligence solutions based on deep learning have demonstrated state-of-the-art results in many application areas, including medical imaging. More specifically, automated tools applied at cine sequences have revolutionized volumetric CMR reporting in the past five years. Applying deep learning models to T1 and T2 mapping images can similarly improve the efficiency of post-processing pipelines and consequently facilitate diagnostic processes. Methods In this paper, we introduce a deep learning model for myocardium segmentation trained on over 7,000 raw CMR images from 262 subjects of heterogeneous disease etiology. The data were labeled by three experts. As part of the evaluation, Dice score and Hausdorff distance among experts is calculated, and the expert consensus is compared with the model's predictions. Results Our deep learning method achieves 86% mean Dice score, while contours provided by three experts on the same data show 90% mean Dice score. The method's accuracy is consistent across epicardial and endocardial contours, and on basal, midventricular slices, with only 5% lower results on apical slices, which are often challenging even for experts. Conclusions We trained and evaluated a deep learning based segmentation model on 262 heterogeneous CMR cases. Applying deep neural networks to T1 and T2 mapping could similarly improve diagnostic practices. Using the fine details of T1 and T2 mapping images and high-quality labels, the objective of this research is to approach human segmentation accuracy with deep learning.
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Affiliation(s)
- András Kalapos
- Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Liliána Szabó
- Semmelweis University, Heart and Vascular Centre, Budapest, Hungary
| | - Zsófia Dohy
- Semmelweis University, Heart and Vascular Centre, Budapest, Hungary
| | - Máté Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Béla Merkely
- Semmelweis University, Heart and Vascular Centre, Budapest, Hungary
- Department of Sports Medicine, Semmelweis University, Budapest, Hungary
| | - Bálint Gyires-Tóth
- Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Hajnalka Vágó
- Semmelweis University, Heart and Vascular Centre, Budapest, Hungary
- Department of Sports Medicine, Semmelweis University, Budapest, Hungary
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Lin A, Pieszko K, Park C, Ignor K, Williams MC, Slomka P, Dey D. Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification. BJR Open 2023; 5:20220021. [PMID: 37396483 PMCID: PMC10311632 DOI: 10.1259/bjro.20220021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 07/04/2023] Open
Abstract
In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.
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Affiliation(s)
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Katarzyna Ignor
- Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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4
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Comprehensive information integration network for left atrium segmentation on LGE CMR images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Argentiero A, Muscogiuri G, Rabbat MG, Martini C, Soldato N, Basile P, Baggiano A, Mushtaq S, Fusini L, Mancini ME, Gaibazzi N, Santobuono VE, Sironi S, Pontone G, Guaricci AI. The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review. J Clin Med 2022; 11:jcm11102866. [PMID: 35628992 PMCID: PMC9147423 DOI: 10.3390/jcm11102866] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
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Affiliation(s)
- Adriana Argentiero
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, 20149 Milan, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60660, USA;
| | - Chiara Martini
- Radiologic Sciences, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Nicolò Soldato
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Paolo Basile
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Andrea Baggiano
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Saima Mushtaq
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Laura Fusini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Maria Elisabetta Mancini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Nicola Gaibazzi
- Department of Cardiology, Azienda Ospedaliero-Universitaria, 43126 Parma, Italy;
| | - Vincenzo Ezio Santobuono
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
| | - Gianluca Pontone
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
- Department of Emergency and Organ Transplantation, University of Bari, 70121 Bari, Italy
- Correspondence:
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Liu Y, Wang W, Luo G, Wang K, Liang D, Li S. Uncertainty-guided symmetric multi-level supervision network for 3D left atrium segmentation in late gadolinium-enhanced MRI. Med Phys 2022; 49:4554-4565. [PMID: 35420165 DOI: 10.1002/mp.15670] [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: 12/06/2021] [Revised: 03/21/2022] [Accepted: 04/06/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Atrial fibrillation is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient non-invasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with atrial fibrillation. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentation results. METHODS In this study, we proposed an uncertainty-guided symmetric multi-level supervision network for 3D LA segmentation in LGE MRI. Firstly, we constructed a symmetric multi-level supervision structure to combine the corresponding features from the encoding and decoding stages to learn the multi-scale representation of LA. Secondly, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on the surface. RESULTS We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best Hausdorff Distance that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparable performance. CONCLUSIONS We proposed an efficient automatic LA segmentation model that consisted of a symmetric multi-level supervision structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multi-level structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on the surface. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yashu Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Gongning Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Dong Liang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, Ontario, N6A 3K7, Canada
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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 D, Peng Y, Guo Y, Sun J. TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00660-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractAutomated segmentation of cardiac pathology in MRI plays a significant role for diagnosis and treatment of some cardiac disease. In clinical practice, multi-modality MRI is widely used to improve the cardiac pathology segmentation, because it can provide multiple or complementary information. Recently, deep learning methods have presented implausible performance in multi-modality medical image segmentation. However, how to fuse the underlying multi-modality information effectively to segment the pathology with irregular shapes and small region at random locations, is still a challenge task. In this paper, a triple-attention-based multi-modality MRI fusion U-Net was proposed to learn complex relationship between different modalities and pay more attention on shape information, thus to achieve improved pathology segmentation. First, three independent encoders and one fusion encoder were applied to extract specific and multiple modality features. Secondly, we concatenate the modality feature maps and use the channel attention to fuse specific modal information at every stage of the three dedicate independent encoders, then the three single modality feature maps and channel attention feature maps are together concatenated to the decoder path. Spatial attention was adopted in decoder path to capture the correlation of various positions. Once more, we employ shape attention to focus shape-dependent information. Lastly, the training approach is made efficient by introducing deep supervision mechanism with object contextual representations block to ensure precisely boundary prediction. Our proposed network was evaluated on the public MICCAI 2020 Myocardial pathology segmentation dataset which involves patients suffering from myocardial infarction. Experiments on the dataset with three modalities demonstrate the effectiveness of fusion mode of our model, and attention mechanism can integrate various modality information well. We demonstrated that such a deep learning approach could better fuse complementary information to improve the segmentation performance of cardiac pathology.
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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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Huang J, Ding W, Lv J, Yang J, Dong H, Del Ser J, Xia J, Ren T, Wong ST, Yang G. Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information. APPL INTELL 2022; 52:14693-14710. [PMID: 36199853 PMCID: PMC9526695 DOI: 10.1007/s10489-021-03092-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2021] [Indexed: 12/24/2022]
Abstract
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.
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Affiliation(s)
- Jiahao Huang
- College of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, 226019 Nantong, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, 264005 Yantai, China
| | - Jingwen Yang
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing, China
| | - Hao Dong
- Center on Frontiers of Computing Studies, Peking University, Beijing, China
| | - Javier Del Ser
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Jun Xia
- Department of Radiology, Shenzhen Second People’s Hospital, The First Afliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Tiaojuan Ren
- College of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China
| | - Stephen T. Wong
- Systems Medicine and Bioengineering Department, Departments of Radiology and Pathology, Houston Methodist Cancer Center, Houston Methodist Hospital, Weill Cornell Medicine, 77030 Houston, TX USA
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
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Chen J, Yang G, Khan H, Zhang H, Zhang Y, Zhao S, Mohiaddin R, Wong T, Firmin D, Keegan J. JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets. IEEE J Biomed Health Inform 2022; 26:103-114. [PMID: 33945491 DOI: 10.1109/jbhi.2021.3077469] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets.
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12
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Peters DC, Lamy J, Sinusas AJ, Baldassarre LA. Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers. Eur Heart J Cardiovasc Imaging 2021; 23:14-30. [PMID: 34718484 DOI: 10.1093/ehjci/jeab221] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022] Open
Abstract
Left atrial (LA) imaging is still not routinely used for diagnosis and risk stratification, although recent studies have emphasized its importance as an imaging biomarker. Cardiovascular magnetic resonance is able to evaluate LA structure and function, metrics that serve as early indicators of disease, and provide prognostic information, e.g. regarding diastolic dysfunction, and atrial fibrillation (AF). MR angiography defines atrial anatomy, useful for planning ablation procedures, and also for characterizing atrial shapes and sizes that might predict cardiovascular events, e.g. stroke. Long-axis cine images can be evaluated to define minimum, maximum, and pre-atrial contraction LA volumes, and ejection fractions (EFs). More modern feature tracking of these cine images provides longitudinal LA strain through the cardiac cycle, and strain rates. Strain may be a more sensitive marker than EF and can predict post-operative AF, AF recurrence after ablation, outcomes in hypertrophic cardiomyopathy, stratification of diastolic dysfunction, and strain correlates with atrial fibrosis. Using high-resolution late gadolinium enhancement (LGE), the extent of fibrosis in the LA can be estimated and post-ablation scar can be evaluated. The LA LGE method is widely available, its reproducibility is good, and validations with voltage-mapping exist, although further scan-rescan studies are needed, and consensus regarding atrial segmentation is lacking. Using LGE, scar patterns after ablation in AF subjects can be reproducibly defined. Evaluation of 'pre-existent' atrial fibrosis may have roles in predicting AF recurrence after ablation, predicting new-onset AF and diastolic dysfunction in patients without AF. LA imaging biomarkers are ready to enter into diagnostic clinical practice.
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Affiliation(s)
- Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Cardiology, Yale School of Medicine, New Haven, CT, USA
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13
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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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14
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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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15
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Craft J, Li Y, Bhatti S, Cao JJ. How to do left atrial late gadolinium enhancement: a review. Radiol Med 2021; 126:1159-1169. [PMID: 34132927 DOI: 10.1007/s11547-021-01383-3] [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: 01/21/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Quantification of left atrial late gadolinium enhancement is a powerful clinical and research tool. Fibrosis burden has been shown to predict the success of pulmonary vein isolation, post-ablation reoccurrence, and major adverse cardiovascular events such as stroke. OVERVIEW The standardized cardiovascular magnetic resonance imaging protocols 2020 update describes the key components of the examination. This review is a more in-depth guide, geared toward building left atrial late gadolinium enhancement imaging from the ground up. The standard protocol consists of the following: localization, pulmonary vein magnetic resonance angiography, cardiac cines, left ventricular, and atrial late gadolinium enhancement. We also review typical segmentation and post-processing techniques, as well as discuss pitfalls, limitations, and potential future innovations in this area. CONCLUSIONS With sufficient experience and optimized protocols, left atrial late gadolinium enhancement imaging is a strong addition to the cardiac magnetic resonance imaging repertoire.
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Affiliation(s)
- Jason Craft
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA.
| | - Yulee Li
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - Salman Bhatti
- The Ohio State University Wexner Medical Center, 410 W 10th Ave, Columbus, OH, 43210, USA
| | - Jie Jane Cao
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA
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16
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Firouznia M, Feeny AK, LaBarbera MA, McHale M, Cantlay C, Kalfas N, Schoenhagen P, Saliba W, Tchou P, Barnard J, Chung MK, Madabhushi A. Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation. Circ Arrhythm Electrophysiol 2021; 14:e009265. [PMID: 33576688 DOI: 10.1161/circep.120.009265] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Marjan Firouznia
- Department of Biomedical Engineering (M.F., A.M.), Case Western Reserve University
| | - Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University
| | - Michael A LaBarbera
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University
| | - Meghan McHale
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.).,Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Catherine Cantlay
- Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Natalie Kalfas
- Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic
| | - Paul Schoenhagen
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University.,Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.).,Imaging Institute (P.S.), Diagnostic Radiology, Cleveland Clinic
| | - Walid Saliba
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - Patrick Tchou
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - John Barnard
- Quantitative Health Sciences, Lerner Research Institute (J.B.), Diagnostic Radiology, Cleveland Clinic
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.A.L., P.S., M.K.C.), Case Western Reserve University.,Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute (M.M., P.S., W.S., P.T., M.K.C.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (M.F., A.M.), Case Western Reserve University.,Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic.,Louis Stokes Cleveland Veterans Administration Medical Center, OH (A.M.)
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17
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Nikan S, Van Osch K, Bartling M, Allen DG, Rohani SA, Connors B, Agrawal SK, Ladak HM. PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2020; 30:739-753. [PMID: 33226942 DOI: 10.1109/tip.2020.3038363] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similarity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study.
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18
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Habijan M, Babin D, Galić I, Leventić H, Romić K, Velicki L, Pižurica A. Overview of the Whole Heart and Heart Chamber Segmentation Methods. Cardiovasc Eng Technol 2020; 11:725-747. [DOI: 10.1007/s13239-020-00494-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022]
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19
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Borra D, Andalò A, Paci M, Fabbri C, Corsi C. A fully automated left atrium segmentation approach from late gadolinium enhanced magnetic resonance imaging based on a convolutional neural network. Quant Imaging Med Surg 2020; 10:1894-1907. [PMID: 33014723 DOI: 10.21037/qims-20-168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background Several studies suggest that the evaluation of left atrial (LA) fibrosis is a relevant information for the assessment of the appropriate strategy in catheter ablation in atrial fibrillation (AF). Late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) is a non-invasive technique, which might be employed for the non-invasive quantification of LA myocardial fibrotic tissue in patients with AF. Nowadays, the analysis of LGE MRI relies on manual tracing of LA boundaries and this procedure is time-consuming and prone to high inter-observer variability given the different degrees of observers' experience, LA wall thickness and data resolution. Therefore, an automated segmentation approach of the atrial cavity for the quantification of scar tissue would be highly desirable. Methods This study focuses on the design of a fully automated LGE MRI segmentation pipeline which includes a convolutional neural network (CNN) based on the successful architecture U-Net. The CNN was trained, validated and tested end-to-end with the data available from the Statistical Atlases and Computational Modelling of the Heart 2018 Atrial Segmentation Challenge (100 cardiac data). Two different approaches were tested: using both stacks of 2-D axial slices and using 3-D data (with the appropriate changes in the baseline architecture). In the latter approach, thanks to the 3-D convolution operator, all the information underlying 3-D data can be exploited. Once the training was completed using 80 cardiac data, a post-processing step was applied on 20 predicted segmentations belonging to the test set. Results By applying the 2-D and 3-D approaches, average Dice coefficient and mean Hausdorff distances were 0.896, 0.914, and 8.98 mm, 8.34 mm, respectively. Volumes of the anatomical LA meshes from the automated analysis were highly correlated with the volumes from ground truth [2-D: r=0.978, y=0.94x+0.07, bias=3.5 ml (5.6%), SD=5.3 mL (8.5%); 3-D: r=0.982, y=0.92x+2.9, bias=2.1 mL (3.5%), SD=5.2 mL (8.4%)]. Conclusions These results suggest the proposed approach is feasible and provides accurate results. Despite the increase of the number of trainable parameters, the proposed 3-D CNN learns better features leading to higher performance, feasible for a real clinical application.
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Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Alice Andalò
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Michelangelo Paci
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33520 Tampere, Finland
| | - Claudio Fabbri
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Cristiana Corsi
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
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20
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Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J 2020; 28:460-472. [PMID: 32648252 DOI: 10.5603/cj.a2020.0093] [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: 02/18/2020] [Revised: 04/29/2020] [Accepted: 05/25/2020] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.
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Affiliation(s)
- Konrad Pieszko
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland. .,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland.
| | - Jarosław Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Jan Budzianowski
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Bogdan Musielak
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Dariusz Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Wojciech Faron
- Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Janusz Rzeźniczak
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland
| | - Paweł Burchardt
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland.,Department of Biology and Environmental Protection, Poznań University of Medical Sciences, ul. Rokietnicka 8, 60-806 Poznań, Poland
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21
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Yang G, Chen J, Gao Z, Li S, Ni H, Angelini E, Wong T, Mohiaddin R, Nyktari E, Wage R, Xu L, Zhang Y, Du X, Zhang H, Firmin D, Keegan J. Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. FUTURE GENERATIONS COMPUTER SYSTEMS : FGCS 2020; 107:215-228. [PMID: 32494091 PMCID: PMC7134530 DOI: 10.1016/j.future.2020.02.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 01/03/2020] [Accepted: 02/02/2020] [Indexed: 05/20/2023]
Abstract
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently ( ∼ 0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.
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Affiliation(s)
- Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Corresponding author at: Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK.
| | - Jun Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 510006, China
| | - Zhifan Gao
- Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada
| | - Hao Ni
- Department of Mathematics, University College London, London, WC1E 6BT, UK
- Alan Turing Institute, London, NW1 2DB, UK
| | - Elsa Angelini
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, SW7 2AZ, UK
| | - Tom Wong
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Raad Mohiaddin
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Eva Nyktari
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
| | - Ricardo Wage
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | | | | | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 510006, China
- Corresponding author.
| | - David Firmin
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Jennifer Keegan
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
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22
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Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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23
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Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert D. Deep Learning for Cardiac Image Segmentation: A Review. Front Cardiovasc Med 2020; 7:25. [PMID: 32195270 PMCID: PMC7066212 DOI: 10.3389/fcvm.2020.00025] [Citation(s) in RCA: 308] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/17/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
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Affiliation(s)
- Chen Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chen Qin
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Huaqi Qiu
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Giacomo Tarroni
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- CitAI Research Centre, Department of Computer Science, City University of London, London, United Kingdom
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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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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Abstract
OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.
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Zhang N, Yang G, Gao Z, Xu C, Zhang Y, Shi R, Keegan J, Xu L, Zhang H, Fan Z, Firmin D. Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI. Radiology 2019; 291:606-617. [PMID: 31038407 DOI: 10.1148/radiol.2019182304] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.
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Affiliation(s)
- Nan Zhang
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Guang Yang
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Zhifan Gao
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Chenchu Xu
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Yanping Zhang
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Rui Shi
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Jennifer Keegan
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Lei Xu
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Heye Zhang
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - Zhanming Fan
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
| | - David Firmin
- From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.)
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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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Vesal S, Ravikumar N, Maier A. Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. ATRIAL SEGMENTATION AND LV QUANTIFICATION CHALLENGES 2019. [DOI: 10.1007/978-3-030-12029-0_35] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2018; 32:187-195. [PMID: 30460430 DOI: 10.1007/s10334-018-0718-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 11/01/2018] [Accepted: 11/08/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. METHODS A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. RESULTS Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively. DISCUSSION Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images.
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Advanced Imaging of the Left Atrium with Cardiac Magnetic Resonance: A Review of Current and Emerging Methods and Clinical Applications. CURRENT RADIOLOGY REPORTS 2018. [DOI: 10.1007/s40134-018-0303-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2018 2018. [DOI: 10.1007/978-3-030-00934-2_51] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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