Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.
Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.
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DOI：
10.1109/tmi.2012.2206822
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发表时间：
2012-10
影响因子：
10.6
通讯作者：
Garvin MK
中科院分区：
文献类型：
--
作者：
Hu Z;Niemeijer M;Abràmoff MD;Garvin MK
分割视神经乳头（ONH）为中心的光谱域光学相干断层扫描（SD-OCT）体积中的视网膜血管是特别具有挑战性的，因为投影神经管开口（NCO）和ONH中心相对较低的可见性。彩色眼底照片在NCO内的区域中提供相对高的血管对比度，但先前尚未用于辅助SD-OCT血管分割过程。因此，在本文中，我们提出了两种方法的视网膜血管的分割SD-OCT卷，每一个利用互补信息眼底照片。在第一种方法（称为配准眼底血管分割方法）中，首先直接在眼底照片上分割血管（使用k-NN像素分类器），并且通过将眼底照片配准到SD-OCT体积，将该血管分割结果映射到SD-OCT体积。在第二种方法（称为多模态血管分割方法）中，在眼底到SD-OCT配准之后，使用来自两种模态的特征用k-NN分类器同时分割血管。通过SD-OCT体积的图论分割方法获得的视网膜内层和神经管开口的三维结构信息与高斯滤波器组和Gabor小波组合使用以生成特征。该方法在15个上进行训练，并在19个随机选择的独立图像对上进行测试，这些图像对来自34名青光眼受试者的SD-OCT体积和眼底图像。基于受试者工作特征（ROC）曲线分析，本配准眼底和多模态血管分割方法[曲线下面积（AUC）分别为0.85和0.89]均显著优于先前两种基于OCT的方法（AUC为0.78和0.83，p < 0.05）。多模态方法总体上表现显著优于其他三种方法（p < 0.05）。
Segmenting retinal vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging due to the projected neural canal opening (NCO) and relatively low visibility in the ONH center. Color fundus photographs provide a relatively high vessel contrast in the region inside the NCO, but have not been previously used to aid the SD-OCT vessel segmentation process. Thus, in this paper, we present two approaches for the segmentation of retinal vessels in SD-OCT volumes that each take advantage of complimentary information from fundus photographs. In the first approach (referred to as the registered-fundus vessel segmentation approach), vessels are first segmented on the fundus photograph directly (using a k-NN pixel classifier) and this vessel segmentation result is mapped to the SD-OCT volume through the registration of the fundus photograph to the SD-OCT volume. In the second approach (referred to as the multimodal vessel segmentation approach), after fundus-to-SD-OCT registration, vessels are simultaneously segmented with a k-NN classifier using features from both modalities. Three-dimensional structural information from the intraretinal layers and neural canal opening obtained through graph-theoretic segmentation approaches of the SD-OCT volume are used in combination with Gaussian filter banks and Gabor wavelets to generate the features. The approach is trained on 15 and tested on 19 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 34 subjects with glaucoma. Based on a receiver operating characteristic (ROC) curve analysis, the present registered-fundus and multimodal vessel segmentation approaches [area under the curve (AUC) of 0.85 and 0.89, respectively] both perform significantly better than the two previous OCT-based approaches (AUC of 0.78 and 0.83, p < 0.05). The multimodal approach overall performs significantly better than the other three approaches (p < 0.05).