The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
Open accessAbstract
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.
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Cites
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
Contrast Limited Adaptive Histogram Equalization
Zuiderveld, K. (1994) · Graphics Gems · doi:10.1016/b978-0-12-336156-1.50061-6 · Cited by 4,192
Cited by
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
Xiao, X., Lian, S., Luo, Z., & Li, S. (2018) · 2018 9th International Conference on Information Technology in Medicine and Education (ITME) · doi:10.1109/itme.2018.00080 · Cited by 963
Convolutional neural networks in medical image understanding: a survey
Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917
A review of the application of deep learning in medical image classification and segmentation
Cai, L., Gao, J., & Zhao, D. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.02.44 · Cited by 587
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.001 · Cited by 567
Applications of deep learning in fundus images: A review
Li, T., Bo, W., Hu, C., Kang, H., Liu, H., Wang, K., & Fu, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101971 · Cited by 396
Metadata
- Authors
- Dasgupta, A., & Singh, S.
- Venue
- 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
- Publisher
- IEEE
- Published
- 2017-04
- Pages
- 248-251
- OpenAlex
- W2556022279
- Cited by
- 260
- References
- 2
- FWCI
- 80.31
- Type
- conference paper
- Language
- en
- Open access
- green
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- Dasgupta, A., & Singh, S. (2017). A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). https://doi.org/10.1109/isbi.2017.7950512
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