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首页> 外文期刊>Medical image analysis >Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map
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Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

机译：使用粗粒粒度扩散图的3D光学相干断层扫描的视网膜内层分割

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摘要

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean±SD) was 8.52 ± 3.13 and 7.56 ± 2.95 urn for the 2D and 3D methods, respectively.
机译：光学相干断层扫描（OCT）是一种强大而非侵入的视网膜成像方法。在本文中，我们介绍了一种基于名为散射图的谱图理论的新变种的快速分割方法。该研究是对描绘黄斑和视神经头部外观的光谱域（SD）OCT图像进行的。呈现的方法不需要基于边缘的图像信息，本地化大多数边界并依赖于区域图像纹理。因此，所提出的方法在低图像对比度或差的层到层图像梯度的情况下表明了鲁棒性。应用于2D和3D OCT数据集的扩散映射由两个步骤组成，一个步骤组成，用于将数据划分为重要且不太重要的部分，另一个用于将内部层定位分成重要的部分。在第一步中，像素/体素以矩形/立方组分组以形成图表节点。图的重量基于像素/体素之间的几何距离和它们平均强度的差异来计算。第一个扩散映射将数据集群分为三个部分，其中第二个是感兴趣的领域。从剩余的计算中消除了另外两个部分。在第二步中，剩余区域经受另一个扩散图评估，并且基于其纹理相似性局部地位。在来自两种患者组（青光眼和法线）的23个数据集上测试了所提出的方法。平均无符号边界定位误差（平均值±SD）分别为2D和3D方法的8.52±3.13和7.56±2.95 URN。

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