The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks
Open accessAbstract
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Cited by
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De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
CE-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 2,267
Artificial intelligence and deep learning in ophthalmology
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313173 · Cited by 1,554
Artificial Intelligence and Deep Learning in Ophthalmology
Wang, Z., Keane, P. A., Chiang, M., Cheung, C. Y., Wong, T. Y., & Ting, D. S. W. (2022) · Artificial Intelligence in Medicine · doi:10.1007/978-3-030-64573-1_200 · Cited by 1,524
Artificial intelligence in retina
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779
Metadata
- Authors
- Roy, A. G., Conjeti, S., Karri, S. P. K., Sheet, D., Katouzian, A., Wachinger, C., & Navab, N.
- Venue
- Biomedical Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2017-07-13
- Volume
- 8
- Issue
- 8
- Pages
- 3627
- ISSN
- 2156-7085 (ISSN-L)
- OpenAlex
- W2606534623
- Cited by
- 613
- References
- 1
- FWCI
- 42.41
- Type
- journal article
- Language
- en
- Open access
- gold
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- Roy, A. G., Conjeti, S., Karri, S. P. K., Sheet, D., Katouzian, A., Wachinger, C., & Navab, N. (2017). ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomedical Optics Express. https://doi.org/10.1364/boe.8.003627
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