Semantic Scholar Open Access 2019 803 sitasi

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen C. Qin Huaqi Qiu G. Tarroni J. Duan +2 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Medicine Engineering Biology

Penulis (7)

Chen Chen

C. Qin

Huaqi Qiu

G. Tarroni

J. Duan

Wenjia Bai

D. Rueckert

Format Sitasi

APA MLA BibTeX

Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W. et al. (2019). Deep Learning for Cardiac Image Segmentation: A Review. https://doi.org/10.3389/fcvm.2020.00025

Akses Cepat

Lihat di Sumber doi.org/10.3389/fcvm.2020.00025

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 803×
Sumber Database: Semantic Scholar
DOI: 10.3389/fcvm.2020.00025
Akses: Open Access ✓