1. DeepLab and Bias Field Correction Based Automatic Cone Photoreceptor Cell Identification with Adaptive Optics Scanning Laser Ophthalmoscope Images
- Author
-
Jing Wang, Zhang Xin, Lina Xing, Yi He, Wanyue Li, Yiwei Chen, and Guohua Shi
- Subjects
Technology ,genetic structures ,Article Subject ,Scanning laser ophthalmoscope ,Computer Networks and Communications ,Computer science ,TK5101-6720 ,01 natural sciences ,Photoreceptor cell ,010309 optics ,03 medical and health sciences ,0302 clinical medicine ,Optics ,Bias field correction ,0103 physical sciences ,medicine ,Electrical and Electronic Engineering ,Adaptive optics ,business.industry ,Cone (formal languages) ,eye diseases ,Identification (information) ,medicine.anatomical_structure ,Telecommunication ,030221 ophthalmology & optometry ,sense organs ,business ,Information Systems - Abstract
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.
- Published
- 2021