Automated Cone and Vessel Analysis in Adaptive Optics Like Retinal Images for Clinical Diagnostics Support
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-23179-7_9
Reference10 articles.
1. Bergeles, C., et al.: Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images. Biomed. Opt. Express 8(6), 3081-3094 (2017). https://doi.org/10.1364/BOE.8.003081. http://opg.optica.org/boe/abstract.cfm?URI=boe-8-6-3081
2. Chen, Y., et al.: Automated cone cell identification on adaptive optics scanning laser ophthalmoscope images based on TV-L1 optical flow registration and k-means clustering. Appl. Sci. 11(5) (2021). https://doi.org/10.3390/app11052259, https://www.mdpi.com/2076-3417/11/5/2259
3. Chen, Y., et al.: DeepLab and bias field correction based automatic cone photoreceptor cell identification with adaptive optics scanning laser ophthalmoscope images. Wirel. Commun. Mob. Comput. 2021, 2034125 (2021). https://doi.org/10.1155/2021/2034125
4. Cunefare, D., Fang, L., Cooper, R.F., Dubra, A., Carroll, J., Farsiu, S.: Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks. Sci. Rep. 7(1), 6620 (2017). https://doi.org/10.1038/s41598-017-07103-0
5. Jonas, J.B., Schneider, U., Naumann, G.O.: Count and density of human retinal photoreceptors. Graefe’s Arch. Clin. Exp. Ophthalmol. 230(6), 505–510 (1992). https://doi.org/10.1007/BF00181769
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