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 (US) 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.
@article{arxiv.1911.03723,
title = {Deep learning for cardiac image segmentation: A review},
author = {Chen Chen and Chen Qin and Huaqi Qiu and Giacomo Tarroni and Jinming Duan and Wenjia Bai and Daniel Rueckert},
journal= {arXiv preprint arXiv:1911.03723},
year = {2020}
}