The DOI matches its Crossref record; 185 of the 185 references it cites were found in a registry or an index.
Medical image segmentation using deep learning: A survey
Open accessAbstract
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
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
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Cited by
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
Zhong, F., He, K., Ji, M., Chen, J., Gao, T., Li, S., Zhang, J., & Li, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-59436-2 · Cited by 37
Szentimrey, Z., Ameri, G., Hong, C. X., Cheung, R. Y. K., Ukwatta, E., & Eltahawi, A. (2023) · Medical Physics · doi:10.1002/mp.16389 · Cited by 17
Panagoulias, D. P., Tsoureli-Nikita, E., Virvou, M., & Tsihrintzis, G. A. (2024) · arXiv · doi:10.48550/arxiv.2403.14243 · Cited by 3
“Segmentation algorithms are crucial in imaging analysis, helping to isolate and analyze specific areas within medical images for more accurate diagnoses (Wang et al., 2022).”
MediSee: Reasoning-based Pixel-level Perception in Medical Images
Tong, Q., Lu, Z., Liu, J., Zheng, Y., & Lu, Z. (2025) · arXiv · doi:10.48550/arxiv.2504.11008 · Cited by 3
Metadata
- Authors
- Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K.
- Venue
- IET Image Processing
- Published
- 2022-01-17
- Volume
- 16
- Issue
- 5
- Pages
- 1243-1267
- ISSN
- 1751-9659 (ISSN-L)
- 1751-9667
- OpenAlex
- W4206693420
- Cited by
- 892
- References
- 185
- FWCI
- 60.14
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022). Medical image segmentation using deep learning: A survey. IET Image Processing. https://doi.org/10.1049/ipr2.12419
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