The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search
Open accessAbstract
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
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Cites
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
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Retinal Imaging and Image Analysis
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Imaging of Macular Diseases with Optical Coherence Tomography
Puliafito, C. A., Hee, M. R., Lin, C. P., Reichel, E., Schuman, J. S., Duker, J. S., Izatt, J. A., Swanson, E. A., & Fujimoto, J. G. (1995) · Ophthalmology · doi:10.1016/s0161-6420(95)31032-9 · Cited by 1,267
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Cited by
Clinically applicable deep learning for diagnosis and referral in retinal disease
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
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
Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C. I., & Ledesma-Carbayo, M. J. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.000892 · Cited by 265
Li, F., Chen, H., Liu, Z., Zhang, X.-D., Jiang, M.-S., Wu, Z.-Z., & Zhou, K.-Q. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.006204 · Cited by 193
Metadata
- Authors
- Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S.
- Venue
- Biomedical Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2017-04-27
- Volume
- 8
- Issue
- 5
- Pages
- 2732
- ISSN
- 2156-7085 (ISSN-L)
- OpenAlex
- W2608854843
- Cited by
- 481
- References
- 44
- FWCI
- 44.68
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S. (2017). Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical Optics Express. https://doi.org/10.1364/boe.8.002732
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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