Learning layer-specific edges for segmenting retinal layers with large deformations
Learning layer-specific edges for segmenting retinal layers with large deformations
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DOI：
10.1364/boe.7.002888
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发表时间：
2016-07-01
影响因子：
3.4
通讯作者：
Chatterjee, Jyotirmoy
中科院分区：
文献类型：
--
作者：
Karri, S. P. K.;Chakraborthi, Debjani;Chatterjee, Jyotirmoy
为了加强传统的基于图形的视网膜层分割，提出了一种基于结构化学习算法的视网膜光学相干层析成像图像特定层边缘检测算法。该算法同时识别各个层及其对应的边缘，从而在1秒内计算出特定于层的边缘。这些边缘增强了经典的基于动态规划的分割，在没有启发式或先验知识的情况下，在层变形、阴影伪影噪声的情况下。我们考虑了Duke的在线数据集，该数据集包含10名糖尿病黄斑水肿受试者的110次B超扫描，其中8个视网膜层由两名专家注释进行实验，平均距离误差为1.38像素，而最先进的距离误差为1.68像素。(C)2016年度美国光学学会
We present an algorithm for layer-specific edge detection in retinal optical coherence tomography images through a structured learning algorithm to reinforce traditional graph-based retinal layer segmentation. The proposed algorithm simultaneously identifies individual layers and their corresponding edges, resulting in the computation of layer-specific edges in 1 second. These edges augment classical dynamic programming based segmentation under layer deformation, shadow artifacts noise, and without heuristics or prior knowledge. We considered Duke's online data set containing 110 B-scans of 10 diabetic macular edema subjects with 8 retinal layers annotated by two experts for experimentation, and achieved a mean distance error of 1.38 pixels whereas that of the state-of-the-art was 1.68 pixels. (C) 2016 Optical Society of America