Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising progress in 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 patchbased Output Space Adversarial Learning framework (pOSAL) 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 pOSAL 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-segmentationbased adversarial loss is insufficient to drive the network to capture segmentation details, we further design the pOSAL in a patch-based fashion to enable fine-grained discrimination on local segmentation details. We extensively evaluate our pOSAL framework and demonstrate its effectiveness in improving the segmentation performance on three public retinal fundus image datasets, i.e., Drishti-GS, RIM-ONE-r3, and REFUGE. Furthermore, our pOSAL framework achieved the first place in the OD and OC segmentation tasks in MICCAI 2018 Retinal Fundus Glaucoma Challenge.
DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup
MoE Key Laboratory of Brain-Inspired Intelligence Perception（脑启发智能感知与认知重点实验室）；Cognition, University of Science（认知，科学大学）；Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA（生物医学工程系，路易斯安那大学，新奥尔良，LA 70118，美国）；School of Mathematics（数学学院）；Computer Sciences, Nanchang University, Nanchang 330031, China（计算机科学系，南昌大学，南昌 330031，中国）
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
University of Florida（佛罗里达大学）；UNC Charlotte（北卡罗来纳大学夏洛特分校）；Xi’an Jiaotong University（西安交通大学）；Sensetime Research（商汤科技研究院）
最近，深度神经网络在注释良好的数据集中表现出与董事会认证的眼科医生相当甚至更好的性能。然而，视网膜成像设备的多样性带来了一个重大挑战：域转移，这导致在将深度学习模型应用于新的测试域时性能下降。在本文中，我们提出了一种新的无监督域自适应框架，称为协作特征集成自适应（CFEA），以有效地克服这一挑战。我们提出的CFEA是一个交互式的范例，通过对抗学习和集成权重呈现出一种精致的协作适应。特别是，我们同时实现了域不变性，并保持了历史预测的指数移动平均值，这通过在训练期间集成权重来实现对未标记数据的更好预测。在不注释来自目标域的任何样本的情况下，编码器和解码器层中的多个对抗性损失引导域不变特征的提取，以混淆域分类器，同时有利于平滑权重的集成。综合实验结果表明，我们的CFEA模型可以克服性能下降，并优于国家的最先进的方法在分割视网膜视盘和杯眼底图像。\textit{Code is available at \url{https://github.com/cswin/AWC}}。
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）
Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup
Youth Laboratories Ltd.（青年实验室有限公司）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；University of Colorado Denver School of Medicine（科罗拉多大学丹佛医学院）；Francis I. Proctor Foundation, University of California San Francisco（加州大学旧金山分校弗朗西斯·I·普罗克特基金会）；University of California San Francisco（加州大学旧金山分校）；Institution of Russian Academy of Sciences Dorodnicyn Computing Centre of RAS（俄罗斯科学院多罗德尼钦计算中心）
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局资讯通信研究院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）
Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes
Laboratoire LIMOS, CNRS UMR 6158（利摩日大学实验室，法国国家科研中心UMR 6158联合研究单位）；Université Clermont-Auvergne（克莱蒙奥弗涅大学）；AL Mansour University College（曼苏尔大学学院）
