The DOI matches its Crossref record; 240 of the 240 references it cites were found in a registry or an index.
Deep semantic segmentation of natural and medical images: a review
Open accessAbstract
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.
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Cites
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Cited by
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095
“, Tajbakhsh et al. (2019a) and Ross et al.”
BackgroundMedical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
Xu, J., Xiong, Z., & Bhattacharyya, S. P. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01871 · Cited by 817
Towards a guideline for evaluation metrics in medical image segmentation
Müller, D., Soto-Rey, I., & Kramer, F. (2022) · BMC Research Notes · doi:10.1186/s13104-022-06096-y · Cited by 668
Metadata
- Authors
- Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G.
- Venue
- Artificial Intelligence Review
- Published
- 2020-06-13
- Volume
- 54
- Issue
- 1
- Pages
- 137-178
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2981227223
- Cited by
- 870
- References
- 240
- FWCI
- 46.83
- Type
- journal article
- Language
- en
- Open access
- green
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- Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021). Deep semantic segmentation of natural and medical images: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-020-09854-1
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