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3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
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Metadata
- Authors
- Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., & Heng, P.-A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Dou, Qi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 149-157
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2463818697
- Cited by
- 344
- References
- 13
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 22 · 2023: 26 · 2022: 32 · 2021: 65 · 2020: 39 · 2019: 67 · 2018: 36 · 2017: 30 · 2016: 4
- FWCI
- 15.85
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- liver segmentation, CT volumes, fully convolutional network, conditional random fields, liver tumor segmentation, computer-aided diagnosis
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