Deep Learning for Cardiac Image Segmentation: A Review

Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert D (2020)

Publication Type: Journal article, Review article

Publication year: 2020

Journal

Frontiers in Cardiovascular Medicine Frontiers Media

Book Volume: 7

Article Number: 25

DOI: 10.3389/fcvm.2020.00025

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Birmingham GB United Kingdom (GB)

How to cite

APA:

Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine, 7. https://doi.org/10.3389/fcvm.2020.00025

MLA:

Chen, Chen, et al. "Deep Learning for Cardiac Image Segmentation: A Review." Frontiers in Cardiovascular Medicine 7 (2020).

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