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首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts
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Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts

机译：在图形切割中使用基于多分辨率纹理的建模来增强深度成像OCT中的脉络膜边界分割

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摘要

The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral domain (SD) OCT machines. A texture-based algorithm is introduced in this paper for fully automatic segmentation of choroidal images obtained from an EDI system of Heidelberg 3D OCT Spectralis. Dynamic programming is utilized to determine the location of the retinal pigment epithelium (RPE). Bruch's membrane (BM) (the blood-retina barrier which separates the RPE cells of the retina from the choroid) can be segmented by searching for the pixels with the biggest gradient value below the RPE. Furthermore, a novel method is proposed to segment the choroid-sclera interface (CSI), which employs the wavelet based features to construct a Gaussian mixture model (GMM). The model is then used in a graph cut for segmentation of the choroidal boundary. The proposed algorithm is tested on 100 EDI OCTs and is compared with manual segmentation. The results showed an unsigned error of 2.48 ± 0.32 pixels for BM extraction and 9.79 ± 3.29 pixels for choroid detection. It implies significant improvement of the proposed method over other approaches like k-means and graph cut methods.
机译：增强深度成像光学相干断层扫描（EDI-OCT）的引入提供了脉络膜的体内横截面成像的优点，类似于视网膜，使用标准的商用光谱域（SD）OCT机器。本文介绍了一种基于纹理的算法，用于对从海德堡3D OCT Spectralis的EDI系统获得的脉络膜图像进行全自动分割。动态编程用于确定视网膜色素上皮（RPE）的位置。布鲁赫膜（BM）（将视网膜RPE细胞与脉络膜分开的血视网膜屏障）可以通过搜索RPE下方具有最大梯度值的像素进行分割。此外，提出了一种新颖的分割脉络膜-巩膜界面（CSI）的方法，该方法利用基于小波的特征来构造高斯混合模型（GMM）。然后将该模型用在图形切割中以用于脉络膜边界的分割。该算法在100个EDI OCT上进行了测试，并与手动分割进行了比较。结果显示，BM提取的无符号误差为2.48±0.32像素，脉络膜检测的无符号误差为9.79±3.29像素。这意味着与其他方法（例如k均值和图割方法）相比，该方法有显着改进。

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