Vessel-Guided Reinforcement Learning-Based Refinement of 3D SegNet for Multi-Class Liver Segmentation
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1× · see citing works
“Accurate delineation of hepatic structures supports reliable volume estimation, lesion localization, and vascular analysis for personalized treatment [7], [10].”
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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 · cited 2× · see citing works
“More recently, deep learning architectures such as U-Net [14], 3D U-Net [4], V-Net [10], and Dense-UNet [8] have significantly improved segmentation accuracy through encoder–decoder designs and volumetric feature learning.”
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Attention U-Net: Learning Where to Look for the Pancreas
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Flagged first, then by citations
Research works printed but not linked: 6
- Chen, Transunet: Transformers make strong encoders for medical image segmentation, 74
- 10.1007/978-3-319-46723-8_49 · DOI · Paper page
- Hayata, 2013, Liver segmentation from 3d ct images using superpixel, IEICE Technical Report; IEICE Tech. Rep, 113, 33
- 10.1109/tmi.2018.2845918 · DOI
- Moawad, 2021, Multimodality annotated hcc cases with and without advanced imaging segmentation [data set]
- 10.1007/978-3-319-24574-4_28 · DOI · Paper page
- Authors
- Wali, S., Khan, M. I., Samina, S., & Raza Sialvi, M. A.
- Affiliations
- Quanzhou University of Information Engineering (CN)
- Quanzhou University of Information Engineering
- Venue
- 2026 11th International Conference on Image, Vision and Computing (ICIVC)
- Publisher
- IEEE
- Published
- 2026-07
- Pages
- 485-491
- OpenAlex
- W7213439490
- Semantic Scholar
- 64bfcebf149bead9c5000b1b21164efd2bd67154
- Cited by
- 0
- References
- 14
- FWCI
- 0.00
- Type
- conference paper
- Open access
- closed
- Licence
- Locations
- International Conference on Image, Vision and Computing (ICIVC) (publishedVersion)
- Funders
- Natural Science Foundation of Fujian Province
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- liver segmentation, multi-class segmentation, vessel segmentation, 3D medical image segmentation, segmentation refinement
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