Open AccessAutomated cone photoreceptor cell identification in confocal adaptive optics scanning laser ophthalmoscope images based on object detection
Author(s) -
Yiwei Chen,
Yi He,
Jing Wang,
Wanyue Li,
Lina Xing,
Xin Zhang,
Guohua Shi
Publication year - 2021
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545822500018
Subject(s) - confocal , adaptive optics , artificial intelligence , identification (biology) , laser scanning , computer vision , optics , scanning laser ophthalmoscopy , computer science , cone (formal languages) , physics , laser , biology , botany , algorithm
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.