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Deep Learning for Cardiac Image Segmentation: A Review - 2020

Deep Learning For Cardiac Image Segmentation: A Review

Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai and Daniel Rueckert

Journal name:  Frontiers in Cardiovascular Medicine

Conferrence name:  

Publisher name:  Frontiers Media S.A

DOI:  10.3389/fcvm.2020.00025

Volume Information: