The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
Abstract
Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. Recently, deep learning based retinal vessel segmentation methods have reached the state-of-the-art performance. Due to the extreme variations in the morphology of the vessels against the noisy background, these methods still have issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc. In this paper, we proposed a U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing these issues. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed methods.
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Cites
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
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
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
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
Hoover, A. D., Kouznetsova, V., & Goldbaum, M. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.845178 · Cited by 2,582
Segmenting Retinal Blood Vessels WithDeep Neural Networks
Liskowski, P., & Krawiec, K. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2546227 · Cited by 890
Clinical Ophthalmology: A Systematic Approach
(1991) · Archives of Ophthalmology · doi:10.1001/archopht.1991.01080100041028 · Cited by 852
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
Fu, H., Xu, Y., Lin, S., Kee Wong, D. W., & Liu, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_16 · Cited by 453
Hierarchical retinal blood vessel segmentation based on feature and ensemble learning
Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., & Yang, G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.07.059 · Cited by 390
Dasgupta, A., & Singh, S. (2017) · 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) · doi:10.1109/isbi.2017.7950512 · Cited by 260
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial NetworksPreprint
Son, J., Park, S. J., & Jung, K.-H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09318 · Cited by 176
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
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662
Zhang, C., Jiang, W., Zhang, Y., Wang, W., Zhao, Q., & Wang, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3144894 · Cited by 325
SegDiff: Image Segmentation with Diffusion Probabilistic Models
Amit, T., Shaharbany, T., Nachmani, E., & Wolf, L. (2021) · arXiv · doi:10.48550/arxiv.2112.00390 · Cited by 179
Metadata
- Authors
- Xiao, X., Lian, S., Luo, Z., & Li, S.
- Venue
- 2018 9th International Conference on Information Technology in Medicine and Education (ITME)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 327-331
- OpenAlex
- W2907750714
- Cited by
- 963
- References
- 16
- FWCI
- 54.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xiao, X., Lian, S., Luo, Z., & Li, S. (2018). Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME). https://doi.org/10.1109/itme.2018.00080
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