Deep Learning for Cardiac Image Segmentation: A Review

Frontiers in Cardiovascular Medicine

Huaqi Qiu¹  Daniel Rueckert¹  Chen Qin¹  Chen Chen¹  Giacomo Tarroni²  Wenjia Bai⁴  Jinming Duan⁵ 

[1] Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom;CitAI Research Centre, Department of Computer Science, City University of London, London, United Kingdom;Data Science Institute, Imperial College London, London, United Kingdom;Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom;School of Computer Science, University of Birmingham, Birmingham, United Kingdom;

关键词: artificial intelligence;    deep learning;    neural networks;    cardiac image segmentation;    cardiac image analysis;    MRI;   

DOI  :  10.3389/fcvm.2020.00025

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown