scholarly journals Automated Cone Photoreceptor Cell Segmentation and Identification in Adaptive Optics Scanning Laser Ophthalmoscope Images Using Morphological Processing and Watershed Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105786-105792
Author(s):  
Yiwei Chen ◽  
Yi He ◽  
Jing Wang ◽  
Wanyue Li ◽  
Lina Xing ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Photoreceptor Cell ◽  
Morphological Processing ◽  
Scanning Laser Ophthalmoscope ◽  
Cell Segmentation ◽  
Scanning Laser ◽  
Watershed Algorithm ◽  
Cone Photoreceptor

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

2021 ◽  
pp. 2250001
Author(s):  
Yiwei Chen ◽  
Yi He ◽  
Jing Wang ◽  
Wanyue Li ◽  
Lina Xing ◽  
...  
Keyword(s):  
Object Detection ◽  
Adaptive Optics ◽  
Photoreceptor Cell ◽  
Ground Truth ◽  
Photoreceptor Cells ◽  
Detection Algorithm ◽  
Scanning Laser Ophthalmoscope ◽  
Scanning Laser ◽  
Cone Photoreceptor ◽  
Cell Identification

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.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yiwei Chen ◽  
Yi He ◽  
Jing Wang ◽  
Wanyue Li ◽  
Lina Xing ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Photoreceptor Cell ◽  
Photoreceptor Cells ◽  
Bias Field ◽  
Eye Diseases ◽  
Scanning Laser Ophthalmoscope ◽  
Scanning Laser ◽  
Cone Photoreceptor ◽  
Cell Identification ◽  
Bias Field Correction

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.

scholarly journals A study of factors affecting the human cone photoreceptor density measured by adaptive optics scanning laser ophthalmoscope

2013 ◽  
Vol 108 ◽  
pp. 1-9 ◽  
Author(s):  
Sung Pyo Park ◽  
Jae Keun Chung ◽  
Vivienne Greenstein ◽  
Stephen H. Tsang ◽  
Stanley Chang
Keyword(s):  
Adaptive Optics ◽  
Scanning Laser Ophthalmoscope ◽  
Scanning Laser ◽  
Cone Photoreceptor ◽  
Factors Affecting
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