Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks

• URL: http://arxiv.org/abs/2007.07502v1
• Date: Wed, 15 Jul 2020 06:21:46 GMT
• Title: Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks
• Authors: Sharath M Shankaranarayana and Keerthi Ram and Kaushik Mitra and Mohanasankar Sivaprakasam
• Abstract summary: We propose a novel method using adversarial network to predict depth map from a single image. We obtain a very high average correlation coefficient of 0.92 upon five fold cross validation. We then use the depth estimation process as a proxy task for joint optic disc and cup segmentation.
• Score: 18.188041599999547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction. Depth is usually obtained explicitly from imaging modalities like optical coherence tomography (OCT) and is very challenging to estimate depth from a single RGB image. To this end, we propose a novel method using adversarial network to predict depth map from a single image. The proposed depth estimation technique is trained and evaluated using individual retinal images from INSPIRE-stereo dataset. We obtain a very high average correlation coefficient of 0.92 upon five fold cross validation outperforming the state of the art. We then use the depth estimation process as a proxy task for joint optic disc and cup segmentation.

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