Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

IEEE Transactions on Medical Imaging
Shujun WangPheng-Ann Heng

Abstract

Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising progress in the joint OD and OC segmentation. However, affected by the domain shift among different datasets, deep networks are severely hindered in generalizing across different scanners and institutions. In this paper, we present a novel patch-based output space adversarial learning framework ( p OSAL) to jointly and robustly segment the OD and OC from different fundus image datasets. We first devise a lightweight and efficient segmentation network as a backbone. Considering the specific morphology of OD and OC, a novel morphology-aware segmentation loss is proposed to guide the network to generate accurate and smooth segmentation. Our p OSAL framework then exploits unsupervised domain adaptation to address the domain shift challenge by encouraging the segmentation in the target domain to be similar to the source ones. Since the whole-segmentation-based adversarial loss is insufficient to drive the network to capture segmentation details, we further design the p OSAL in a patch-b...Continue Reading

Citations

May 24, 2020·International Journal of Computer Assisted Radiology and Surgery·Shreya KadambiEric Xing
Jul 29, 2019·Medical & Biological Engineering & Computing·Wei ZhouWenle Wang
Feb 2, 2021·Medical Image Analysis·Tao LiHuazhu Fu
May 9, 2021·International Journal of Computer Assisted Radiology and Surgery·Ling LuoXinglong Feng
Jul 9, 2021·Journal of Medical Internet Research·Ali Q SaeedAhmad Tarmizi Abdul Ghani
Sep 1, 2021·Medical Image Analysis·Luyan LiuYefeng Zheng

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