Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and researchers to monitor the choroidal thickness in many ocular diseases for diagnosis and management. However, manual segmentation and thickness profiling of choroid are time-consuming which lead to low efficiency in analyzing a large quantity of OCT images for swift treatment of patients. In this paper, an automatic segmentation approach based on convolutional neural network (CNN) classifier and - () fitter is presented to identify boundaries of the choroid and to generate thickness profile of the choroid from retinal OCT images. The method of detecting inner choroidal surface is motivated by its biological characteristics after light reflection, while the outer chorioscleral interface segmentation is transferred into a classification and fitting problem. The proposed method is tested in a data set of clinically obtained retinal OCT images with ground-truth marked by clinicians. Our numerical results demonstrate the effectiveness of the proposed approach to achieve stable and clinically accurate autosegmentation of the choroid.

中文翻译：

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基于CNN分类器和-Fitter的视网膜OCT图像脉络膜分割

光学相干断层扫描 (OCT) 是一种无创横断面成像技术，用于检查眼睛的视网膜结构和病理。使用 OCT 图像评估脉络膜厚度对于临床医生和研究人员监测许多眼部疾病的脉络膜厚度以进行诊断和管理具有重要意义。然而，脉络膜的手动分割和厚度分析非常耗时，这导致分析大量 OCT 图像以快速治疗患者的效率低下。本文提出了一种基于卷积神经网络（CNN）分类器和-（)拟合器用于识别脉络膜的边界并从视网膜 OCT 图像生成脉络膜的厚度剖面。检测内脉络膜表面的方法是由其光反射后的生物学特性激发的，而外脉络膜巩膜界面分割则转化为分类和拟合问题。所提出的方法在临床获得的视网膜 OCT 图像的数据集中进行了测试，这些图像具有临床医生标记的真实情况。我们的数值结果证明了所提出的方法在实现脉络膜的稳定和临床准确的自动分割方面的有效性。