Depth Mapping Hybrid Deep Learning Method for Optic Disc and Cup Segmentation on Stereoscopic Ocular Fundus.

Optic disc and cup segmentation on ocular fundus images is an important prerequisite for diagnosing glaucoma. For the segmentation of optic disc (OD) and optic cup (OC), many previously proposed deep learning methods typically utilize monoscopic view images that lack spatial depth information, limiting their diagnostic ability and overall performance. According to ophthalmologists’ clinical insights, stereoscopic view of ocular fundus contains great potential to improve optic cup segmentation. We propose a depth mapping hybrid (DeMaH) deep learning method that effectively adopts depth mappings to segment OD and OC (ODC) on ocular fundus images. Experimental results demonstrate that our method achieves significant improvement on ODC segmentation, especially OC segmentation, validating the effectiveness of our method to incorporate clinical prior knowledge.