The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Open accessAbstract
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.
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Deep Residual Learning for Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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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
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
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×
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MethodRefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
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Cited by
Swin-Unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25066-8_9 · Cited by 5,640
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
“Several efforts have used atrous convolutional layers [9, 27, 18] to enlarge the”
MethodUNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H., & Xu, D. (2021) · arXiv · doi:10.48550/arxiv.2103.10504 · Cited by 3,580
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
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Self-citationU-Net-Based Medical Image Segmentation
Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4189781 · Cited by 318
Retracted
Metadata
- Authors
- Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., & Liu, J.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2019-10
- Volume
- 38
- Issue
- 10
- Pages
- 2281-2292
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2921406441
- Cited by
- 2,267
- References
- 15
- FWCI
- 86.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., & Liu, J. (2019). CE-Net: Context Encoder Network for 2D Medical Image Segmentation. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2019.2903562
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