Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology
Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology
复制标题
DOI：
10.1364/boe.5.000348
复制
发表时间：
2014-02-01
影响因子：
3.4
通讯作者：
Farsiu, Sina
中科院分区：
文献类型：
--
作者：
Srinivasan, Pratul P.;Heflin, Stephanie J.;Farsiu, Sina
在光学相干断层扫描（OCT）上准确定量小鼠视网膜层厚度对于许多眼部和神经系统疾病的研究至关重要。然而，手工分割是费时且主观的。以前自动化这一过程的尝试仅限于对没有缺失层或可见病理的小鼠进行高质量扫描。本文提出了一种基于稀疏度去噪、支持向量机、图论和动态规划（S-GTDP）的光谱域OCT图像视网膜层自动分割方法。结果表明，与人工分割相比，该方法准确地分割了野生型和视紫红质敲除小鼠的所有视网膜层边界，范围从7到10，与商业自动化Diver分割软件相比，该方法具有更准确的性能。(C) 2014年美国光学学会
Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software. (C) 2014 Optical Society of America