Automated cone photoreceptor cell identification in confocal adaptive optics scanning laser ophthalmoscope images based on object detection

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 [Formula: see text]-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.

DeepLab and Bias Field Correction Based Automatic Cone Photoreceptor Cell Identification with Adaptive Optics Scanning Laser Ophthalmoscope Images

The identification of cone photoreceptor cells is important for early diagnosing of eye diseases. We proposed automatic deep-learning cone photoreceptor cell identification on adaptive optics scanning laser ophthalmoscope images. The proposed algorithm is based on DeepLab and bias field correction. Considering manual identification as reference, our algorithm is highly effective, achieving precision, recall, and F 1 score of 96.7%, 94.6%, and 95.7%, respectively. To illustrate the performance of our algorithm, we present identification results for images with different cone photoreceptor cell distributions. The experimental results show that our algorithm can achieve accurate photoreceptor cell identification on images of human retinas, which is comparable to manual identification.

Automated Cone Cell Identification on Adaptive Optics Scanning Laser Ophthalmoscope Images Based on TV-L1 Optical Flow Registration and K-Means Clustering

Cone cell identification is essential for diagnosing and studying eye diseases. In this paper, we propose an automated cone cell identification method that involves TV-L1 optical flow estimation and K-means clustering. The proposed algorithm consists of the following steps: image denoising based on TV-L1 optical flow registration, bias field correction, cone cell identification based on K-means clustering, duplicate identification removal, identification based on threshold segmentation, and merging of closed identified cone cells. Compared with manually labelled ground-truth images, the proposed method shows high effectiveness with precision, recall, and F1 scores of 93.10%, 94.97%, and 94.03%, respectively. The method performance is further evaluated on adaptive optics scanning laser ophthalmoscope images obtained from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy. The evaluation results demonstrate that the proposed method can accurately identify cone cells in subjects with healthy retinas and retinal diseases.

Recent Advances and Clinical Application of Color Scanning Laser Ophthalmoscope

Scanning laser ophthalmoscopes (SLOs) have been available since the early 1990s, but they were not commonly used because their advantages were not enough to replace conventional color fundus photography. In recent years, color SLOs have improved significantly, and the colored SLO images are obtained by combining multiple SLO images taken by lasers of different wavelengths. A combination of these images of different lasers can create an image that is close to that of the real ocular fundus. One advantage of the advanced SLOs is that they can obtain images with a wider view of the ocular fundus while maintaining a high resolution even through non-dilated eyes. The current SLOs are superior to the conventional fundus photography in their ability to image abnormal alterations of the retina and choroid. Thus, the purpose of this review was to present the characteristics of the current color SLOs and to show how that can help in the diagnosis and the following of changes after treatments. To accomplish these goals, we will present our findings in patients with different types of retinochoroidal disorders.