The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
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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
(2010) · Cited by 14,134
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 1×
“To sufficiently encode 3D spatial information which is crucial for volumetric image analysis, 3D CNNs have been very recently proposed in medical imaging community and successfully employed on brain lesion analysis applications [3,8].”
MethodEfficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908
Comparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679
Self-citationDCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Chen, H., Qi, X., Yu, L., & Heng, P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.273 · Cited by 671
Self-citationShape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model
Kainmüller, D., Lange, T., & Lamecker, H. (2007) · Cited by 173
A Generic Probabilistic Active Shape Model for Organ Segmentation
Wimmer, A., Soza, G., & Hornegger, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3_4 · Cited by 74
A Statistical Deformable Model for the Segmentation of Liver CT Volumes
Heimann, T., Meinzer, H., & Wolf, I. (2007) · Cited by 69
Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks
Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_72 · Cited by 64
Al-Shaikhli, S. D. S., Yang, M. Y., & Rosenhahn, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.01521 · Cited by 30
Research works printed but not linked: 1
- Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. arXiv preprint arXiv:1409.5185 (2014) · arXiv
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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
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2959609 · Cited by 4,631
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00181 · Cited by 3,586
“CNN-based Segmentation Networks : Since the introduction of the seminal U-Net [36], CNN-based networks have achieved state-of-the-art results on various 2D and 3D various medical image segmentation tasks [15, 54, 49, 17, 28].”
MethodH-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., & Heng, P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,530
Self-citationCE-Net: Context Encoder Network for 2D Medical Image Segmentation
Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., & Liu, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2903562 · Cited by 2,433
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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