PM-Net: Pyramid Multi-label Network for Joint Optic Disc and Cup Segmentation

Accurate segmentation of optic disc (OD) and optic cup (OC) is a fundamental task for fundus image analysis. Most existing methods focus on segmenting OD and OC inside the optic nerve head (ONH) area but paying little attention to accurate ONH localization. In this paper, we propose a Mask-RCNN based paradigm to localize ONH and jointly segment OD and OC in a whole fundus image. However, directly using Mask-RCNN faces some critical issues: First, for some glaucoma cases, the highly overlapping of OD and OC may lead to the missing of OC proposals. Second, some proposals may not fully surround the object, and thus the segmentation can be incomplete. Last, the instance head in Mask-RCNN cannot well incorporate the prior such as the OC is inside the OD. To address these issues, we first propose a segmentation based region proposal network (RPN) to improve the accuracy of proposals and then propose a pyramid RoIAlign module to aggregate the multi-level information to get a better feature representation. Furthermore, we employ a multi-label head strategy to incorporate the prior for better performance. Extensive experiments verify our method.