Deep learning for cardiac image segmentation: A review

TL;DR

This review comprehensively covers deep learning methods for cardiac image segmentation across various imaging modalities, datasets, and anatomical structures, highlighting current challenges and future research directions.

Contribution

It provides an extensive survey of over 100 papers, summarizes datasets and code repositories, and discusses key challenges in the field.

Findings

Deep learning dominates cardiac image segmentation research.

Public datasets and code repositories are summarized to promote reproducibility.

Challenges include label scarcity, model generalizability, and interpretability.

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 (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.