Soltanian-Zadeh Somayyeh, Liu Zhuolin, Liu Yan, Lassoued Ayoub, Cukras Catherine A, Miller Donald T, Hammer Daniel X, Farsiu Sina
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
Biomed Opt Express. 2023 Jan 23;14(2):815-833. doi: 10.1364/BOE.478693. eCollection 2023 Feb 1.
Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.
对视锥细胞形态进行客观量化，如细胞直径和外段长度，对于视网膜神经退行性疾病的早期、准确和灵敏诊断及预后评估至关重要。自适应光学光学相干断层扫描（AO-OCT）能够对活体人眼的视锥细胞进行三维（3-D）可视化成像。从AO-OCT图像中提取细胞形态的当前金标准涉及二维手动标记这一繁琐过程。为实现该过程的自动化并扩展到对体数据的三维分析，我们提出了一个全面的深度学习框架，用于在AO-OCT扫描中分割单个视锥细胞。我们的自动化方法在评估使用代表两种不同类型点扫描OCT（光谱域和扫频光源）的三种不同AO-OCT系统采集的健康和患病参与者的视锥光感受器时，达到了人类水平的性能。
