Deep-learning based volumetric quantification of retinal lesions in murine model of focal laser injury

Jose J. Rico-Jimenez; Dewei Hu; Edward M. Levine; Ipek Oguz; Yuankai K. Tao

doi:10.1117/12.2607327

7 March 2022 Deep-learning based volumetric quantification of retinal lesions in murine model of focal laser injury

Jose J. Rico-Jimenez, Dewei Hu, Edward M. Levine, Ipek Oguz, Yuankai K. Tao

Author Affiliations +

Proceedings Volume PC11941, Ophthalmic Technologies XXXII; PC1194108 (2022) https://doi.org/10.1117/12.2607327
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

We present a deep-learning based approach for automated qualitative assessment of lesion volumes using OCT images to enable real-time assessment of injury severity and longitudinal tracking of tissues response to photodamage. The network has been trained to quantify photodamage between the outer plexiform layer (OPL) and retinal pigmented epithelium (RPE) accurately without the need for extensive image pre- and post-processing. Manually annotated OCT cross-sections were used as ground-truths to train a U-Net convolutional neural network. The network was designed and implemented in PyTorch based on the multi-scale U-Net architecture.

Conference Presentation

Citation Download Citation

Jose J. Rico-Jimenez, Dewei Hu, Edward M. Levine, Ipek Oguz, and Yuankai K. Tao "Deep-learning based volumetric quantification of retinal lesions in murine model of focal laser injury", Proc. SPIE PC11941, Ophthalmic Technologies XXXII, PC1194108 (7 March 2022); https://doi.org/10.1117/12.2607327

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