The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Deep Learning for Cardiac Image Segmentation: A Review
Open accessAbstract
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.
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Cites
Deep Residual Learning for Image Recognition
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Densely Connected Convolutional Networks
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Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Rethinking the Inception Architecture for Computer Vision
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Cited by
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
Campello, V. M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P. M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3090082 · Cited by 481
Dynamic Memory to Alleviate Catastrophic Forgetting in Continual Learning with Medical Imaging
Perkonigg, M., Hofmanninger, J., Herold, C. J., Brink, J. A., Pianykh, O., Prosch, H., & Langs, G. (2021) · Nature Communications · doi:10.1038/s41467-021-25858-z · Cited by 106
Alkhodari, M., Xiong, Z., Khandoker, A. H., Hadjileontiadis, L. J., Leeson, P., & Lapidaire, W. (2023) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2023.2223978 · Cited by 23
Marey, A., Serdysnki, K. C., Killeen, B. D., Unberath, M., & Umair, M. (2024) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubae008 · Cited by 7
Metadata
- Authors
- Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D.
- Venue
- Frontiers in Cardiovascular Medicine
- Publisher
- Frontiers Media SA
- Published
- 2020-03-05
- Volume
- 7
- Pages
- 25-25
- ISSN
- 2297-055X (ISSN-L)
- OpenAlex
- W3009563704
- Cited by
- 869
- References
- 32
- FWCI
- 45.75
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine. https://doi.org/10.3389/fcvm.2020.00025
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