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A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT

机译：SD-OCT中视网膜分支动脉闭塞的分类和分割框架

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Branch retinal artery occlusion (BRAO) is an ocular emergency which could lead to blindness. Quantitative analysis of BRAO region in the retina is very needed to assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to classify and segment BRAO based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, a support vector machine (SVM) based classifier is designed to differentiate BRAO into acute phase and chronic phase, and the two types are segmented separately. To segment BRAO in chronic phase, a threshold-based method is proposed based on the thickness of inner retina. While for segmenting BRAO in acute phase, a two-step segmentation is performed, which includes the bayesian posterior probability based initialization and the graph-search-graph-cut based segmentation. The proposed method was tested on SD-OCT images of 23 patients (12 of acute and 11 of chronic phase) using leave-one-out strategy. The overall classification accuracy of SVM classifier was 87.0%, and the TPVF and FPVF for acute phase were 91.1%, 5.5%; for chronic phase were 90.5%, 8.7%, respectively.
机译：分支视网膜动脉闭塞（BraO）是一种眼部紧急情况，可能导致失明。视网膜中Brao区的定量分析是评估视网膜缺血的严重程度。在本文中，提出了一种全自动框架，以基于3D光谱域光学相干断层扫描（SD-OCT）图像对BRAO进行分类。据我们所知，这是第一个自动3D BRAO分段框架。首先，设计基于支持向量机（SVM）的分类器以区分BRAO进入急性期和慢性阶段，并且两种类型分别分割。在慢性阶段进行分段BRAO，基于内视网膜的厚度提出了一种基于阈值的方法。虽然在急性相中分割BRAO的同时，执行两步分割，其包括基于贝叶斯后概率的初始化和基于图形搜索图的分割。使用休次次策略对23名患者（急性和11例）的SD-OCT图像进行测试。 SVM分类器的整体分类精度为87.0％，TPVF和急性阶段的FPVF为91.1％，5.5％;对于慢性相分别为90.5％，8.7％。

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