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      Deep Learning for Cardiac Image Segmentation: A Review

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          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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          Deep Residual Learning for Image Recognition

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            Attention Is All You Need

            The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
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              Deep Learning in Medical Image Analysis

              This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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                Author and article information

                Contributors
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                05 March 2020
                2020
                : 7
                : 25
                Affiliations
                [1] 1Biomedical Image Analysis Group, Department of Computing, Imperial College London , London, United Kingdom
                [2] 2CitAI Research Centre, Department of Computer Science, City University of London , London, United Kingdom
                [3] 3School of Computer Science, University of Birmingham , Birmingham, United Kingdom
                [4] 4Data Science Institute, Imperial College London , London, United Kingdom
                [5] 5Department of Brain Sciences, Faculty of Medicine, Imperial College London , London, United Kingdom
                Author notes

                Edited by: Karim Lekadir, University of Barcelona, Spain

                Reviewed by: Jichao Zhao, The University of Auckland, New Zealand; Marta Nuñez-Garcia, Institut de Rythmologie et Modélisation Cardiaque (IHU-Liryc), France

                *Correspondence: Chen Chen chen.chen15@ 123456imperial.ac.uk

                This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine

                Article
                10.3389/fcvm.2020.00025
                7066212
                32195270
                3edcf13c-3f74-48d6-9f65-73cd25fe44b2
                Copyright © 2020 Chen, Qin, Qiu, Tarroni, Duan, Bai and Rueckert.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 October 2019
                : 17 February 2020
                Page count
                Figures: 8, Tables: 7, Equations: 3, References: 257, Pages: 33, Words: 27476
                Funding
                Funded by: Engineering and Physical Sciences Research Council 10.13039/501100000266
                Award ID: EP/P001009/1
                Award ID: EP/R005982/1
                Categories
                Cardiovascular Medicine
                Review

                artificial intelligence,deep learning,neural networks,cardiac image segmentation,cardiac image analysis,mri,ct,ultrasound

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