Deep Learning for Cardiac Image Segmentation: A Review
By Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert. Frontiers in Cardiovascular Medicine, 2019
844 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/47141ea58f79039c6ab1a51c23dc9c660357ff71
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Klaus Hermann Maier-Hein
- Tao Lei
- Bjoern H Menze
- Asoke Kumar Nandi
- Alberto Albiol
- Sotirios A. Tsaftaris
- Yong Wan
- Risheng Wang
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge (2021, positive): “In the last few years, the advent of the deep learning paradigm has motivated the development of many neural network based techniques for improved CMR segmentation, as listed in a recent review [5].”
- Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “and brain-tumor segmentation [3] [4], optic disc segmentation [5] [6], cell segmentation [7] [8], lung segmentation, pulmonary nodules [9] [10], cardiac image segmentation [11] [12], etc.”