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| Publications [#337171] of Sina Farsiu
Papers Published
- Cunefare, D; Langlo, CS; Patterson, EJ; Blau, S; Dubra, A; Carroll, J; Farsiu, S, Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.,
Biomedical optics express, vol. 9 no. 8
(August, 2018),
pp. 3740-3756 [doi]
(last updated on 2024/12/31)
Abstract: Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading.
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