Joint retina segmentation and classification for early glaucoma diagnosis
Joint retina segmentation and classification for early glaucoma diagnosis
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DOI：
10.1364/boe.10.002639
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发表时间：
2019-05-01
影响因子：
3.4
通讯作者：
Zhang, Xiulan
中科院分区：
文献类型：
--
作者：
Wang, Jie;Wang, Zhe;Zhang, Xiulan
提出了一种基于光学相干断层扫描(OCT)视网膜成像的早期青光眼联合分割分类深度模型。我们的动机源于眼科医生通过分析OCT图像中的视网膜神经纤维层(RNFL)做出临床决定的观察。为了模拟这一过程，我们提出了一种新的结合视网膜层分割和青光眼分类的深度模型。我们的模型由三部分组成。首先，分割网络同时预测六个视网膜层和它们之间的五个边界。然后，我们引入了一种后处理算法来融合这两个结果，同时加强了拓扑的正确性。最后，分类网络将RNFL厚度向量作为输入，输出患青光眼的概率。在分类网络中，我们提出了一个精心设计的模块来实现青光眼诊断的临床策略。我们在收集的234名受试者的1004次环形OCT B超和10名糖尿病黄斑水肿患者的110次B超的公共数据集上验证了我们的方法。实验结果表明，无论是在我们收集的数据集上，还是在具有严重视网膜病变的公共数据集上，我们的方法都取得了比其他最先进的方法更好的分割性能。对于青光眼分类，我们的模型达到了81.4%的诊断准确率，AUC值为0.864，明显优于基线方法。(C)OSA开放获取出版协议条款下的2019年美国光学学会
We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement