Cone photoreceptor cell identification is important for the early diagnosis of retinopathy. In this study, an object detection algorithm is used for cone cell identification in confocal adaptive optics scanning laser ophthalmoscope (AOSLO) images. An effectiveness evaluation of identification using the proposed method reveals precision, recall, and F1-score of 95.8%, 96.5%, and 96.1%, respectively, considering manual identification as the ground truth. Various object detection and identification results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method. Overall, the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images, being comparable to manual identification.

中文翻译：

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基于物体检测的共焦自适应光学扫描激光检眼镜图像中的自动锥形感光细胞识别

视锥细胞的识别对于视网膜病变的早期诊断很重要。在这项研究中，对象检测算法用于共焦自适应光学扫描激光检眼镜 (AOSLO) 图像中的视锥细胞识别。使用所提出的方法对识别的有效性评估揭示了精确度、召回率和F1- 将人工识别作为基本事实的得分分别为 95.8%、96.5% 和 96.1%。来自具有不同视锥细胞分布的图像的各种目标检测和识别结果进一步证明了所提出方法的性能。总体而言，所提出的方法可以在共聚焦自适应光学扫描激光检眼镜图像上准确识别锥形感光细胞，与人工识别相媲美。