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....

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Bibliographic Details
Published in:Wireless communications and mobile computing 2021, Vol.2021 (1)
Main Authors: Chen, Yiwei, He, Yi, Wang, Jing, Li, Wanyue, Xing, Lina, Zhang, Xin, Shi, Guohua
Format: Article
Language:English
Subjects:
Accuracy
Adaptive optics
Algorithms
Bias
Datasets
Eye diseases
Identification
Lasers
Optics
Photoreceptors
Scanning
Science
Semantics
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Summary: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 F1 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.
ISSN:1530-8669
1530-8677
DOI:10.1155/2021/2034125