Title
PM-Net: Pyramid Multi-label Network for Joint Optic Disc and Cup Segmentation
Abstract
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.
Year
DOI
Venue
2019
10.1007/978-3-030-32239-7_15
Lecture Notes in Computer Science
Keywords
DocType
Volume
Medical image process,Fundus image,Optic disc,Segmentation
Conference
11764
ISSN
Citations 
PageRank 
0302-9743
2
0.37
References 
Authors
0
7
Name
Order
Citations
PageRank
Pengshuai Yin1171.53
Wu Qingyao225933.46
Yanwu Xu3566.59
Huaqing Min424336.37
Ming Yang5114.10
Yubing Zhang620.37
Rui Tang718819.22