At the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

目前，光学相干断层扫描 (OCT) 是最常用的非侵入性成像方法之一，用于获取人类视网膜组织和脉管系统的大体积扫描。视神经乳头和黄斑处可获取的高分辨率 3D 样本的大量增加与早期眼部疾病检测的医学进步直接相关。为了从提取的 OCT 体积中解析决定性信息并使其适用于进一步的诊断分析，视网膜层厚度的精确测量是对每个患者分别进行的一项基本任务。然而，OCT 扫描的手动检查是一项艰巨且耗时的任务，通常由于存在组织相关的散斑噪声而变得困难。所以，自动分割模型的制定已成为医学图像处理领域的一项重要任务。我们提出了一种新颖的、纯数据驱动的几何方法顺序受限3 d OCT视网膜细胞层分割它将任何度量空间中的输入数据作为输入数据，并且可以仅使用简单的、高度可并行化的操作来实现。与许多已建立的视网膜层分割方法相反，我们仅使用局部提取的特征作为输入，并且不使用任何全局形状先验。视网膜细胞层和膜的生理顺序是通过引入平滑能量项来实现的。这与局部平滑度的额外正则化相结合，以产生高度准确的 3D 分割。因此，该方法系统地避免了与全局形状有关的偏差，因此适用于检测视网膜组织结构的解剖变化。为了证明其稳健性，我们在健康人类视网膜的手动注释 3D OCT 体积数据集上比较了两种不同的特征选择。在平均绝对误差和 Dice 相似系数方面，将计算分割的质量与自动视网膜层分割中的现有技术以及手动注释的地面实况数据进行比较。还提供了分段卷的可视化。