Journal of Innovative Optical Health Sciences, Volume. 15, Issue 1, 2250001(2022)

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

[in Chinese]1... [in Chinese]1,*, [in Chinese]1,2, [in Chinese]1,2,3, [in Chinese]1, [in Chinese]1, and [in Chinese]1,23 |Show fewer author(s)
Author Affiliations
  • 1Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
  • 2Department of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230041, P. R. China
  • 3Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P. R. China
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    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 F1-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.

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    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Automated cone photoreceptor cell identification in confocal adaptive optics scanning laser ophthalmoscope images based on object detection[J]. Journal of Innovative Optical Health Sciences, 2022, 15(1): 2250001

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    Paper Information

    Received: Jun. 30, 2021

    Accepted: Aug. 30, 2021

    Published Online: Mar. 1, 2022

    The Author Email: (heyi_job@126.com)

    DOI:10.1142/s1793545822500018

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