Multiple-object geometric deformable model for segmentation of macular OCT
Multiple-object geometric deformable model for segmentation of macular OCT
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DOI：
10.1364/boe.5.001062
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发表时间：
2014-04-01
影响因子：
3.4
通讯作者：
Prince, Jerry L.
中科院分区：
文献类型：
--
作者：
Carass, Aaron;Lang, Andrew;Prince, Jerry L.
光学相干断层扫描（OCT）是视网膜眼病眼科评估的事实上的标准成像模式，并且在神经系统疾病的研究中越来越重要。黄斑立方体内的各种视网膜层的厚度的量化为许多疾病提供了独特的诊断见解，但自动分割和量化的能力仍然非常有限。虽然手动分割已被用于许多科学研究，但它非常耗时，并且受到评估者内和评估者间变化的影响。本文提出了一种新的计算域，称为平坦的空间，并使用最近开发的多个对象的变形模型方法的黄斑立方体中的特定视网膜层的分割方法。该框架维护对象关系和拓扑结构，同时防止重叠和间隙。该算法在整个黄斑立方体上分割八个视网膜层，其中每个边界都以亚体素精度定义。对37名受试者的单眼OCT扫描的方法进行评估，每个受试者都有手动地面实况，显示了对最先进方法的改进。(C)2014年美国光学学会
Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed deformable model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method. (C) 2014 Optical Society of America