Cell-level quantitative features of retinal ganglion cells (GCs) are potentially important biomarkers for improved diagnosis and treatment monitoring of neurodegenerative diseases such as glaucoma, Parkinson’s disease, and Alzheimer’s disease. Yet, due to limited resolution, individual GCs cannot be visualized by commonly used ophthalmic imaging systems, including optical coherence tomography (OCT), and assessment is limited to gross layer thickness analysis. Adaptive optics OCT (AO-OCT) enables in vivo imaging of individual retinal GCs. We present an automated segmentation of GC layer (GCL) somas from AO-OCT volumes based on weakly supervised deep learning (named WeakGCSeg), which effectively utilizes weak annotations in the training process. Experimental results show that WeakGCSeg is on par with or superior to human experts and is superior to other state-of-the-art networks. The automated quantitative features of individual GCLs show an increase in structure–function correlation in glaucoma subjects compared to using thickness measures from OCT images. Our results suggest that by automatic quantification of GC morphology, WeakGCSeg can potentially alleviate a major bottleneck in using AO-OCT for vision research.

中文翻译：

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用于青光眼损伤评估的自适应光学 OCT 图像的弱监督个体神经节细胞分割

视网膜神经节细胞 (GC) 的细胞水平定量特征可能是改善青光眼、帕金森病和阿尔茨海默病等神经退行性疾病诊断和治疗监测的重要生物标志物。然而，由于分辨率有限，单个 GC 无法通过常用的眼科成像系统（包括光学相干断层扫描 (OCT)）进行可视化，并且评估仅限于总层厚度分析。自适应光学 OCT (AO-OCT) 可在体内实现单个视网膜 GC 的成像。我们提出了一种基于弱监督深度学习（名为 WeakGCSeg）的 AO-OCT 卷中的 GC 层（GCL）体的自动分割，它在训练过程中有效地利用了弱注释。实验结果表明，WeakGCSeg 与人类专家相当或优于人类专家，并且优于其他最先进的网络。与使用 OCT 图像的厚度测量相比，单个 GCL 的自动定量特征显示青光眼受试者的结构-功能相关性增加。我们的结果表明，通过自动量化 GC 形态，WeakGCSeg 可以潜在地缓解使用 AO-OCT 进行视觉研究的主要瓶颈。