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Updated: May 31, 2026
Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
Published on: March 11, 2016
Validation of a Rule-Based Automated Method for RPE Cell Detection Using Adaptive Optics Transscleral Flood
Masakazu Hiraoka1,2, Marika Taniguchi1, Leandre Fura3
1Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo, Japan.
Purpose:
The purpose of this study was to validate a rule-based automated method for detecting retinal pigment epithelium (RPE) cells in adaptive optics (AOs) transscleral flood illumination (AO-TFI) images by comparison with manual annotation and inter-grader agreement.
Methods:
This cross-sectional study analyzed AO-TFI images from six eyes with retinitis pigmentosa (RP) and five healthy eyes. Regions of interest (ROIs) meeting predefined image quality criteria were selected. After background correction and contrast inversion, RPE cells were detected using a rule-based approach based on local maxima detection and distance filtering. Detection parameters were optimized using 30 ROIs and applied without modification to an independent validation set of 39 ROIs. Automated detections were compared with manual annotations (ground truth [GT]) using nearest-neighbor matching, and Precision, Recall, and F1-score were calculated. Inter-grader agreement between two graders (GT1 versus GT2) was assessed.
Results:
Automated detection showed stable performance across ROIs. F1-scores ranged from 0.55 to 0.89 in healthy eyes and from 0.70 to 0.92 in RP eyes, with a micro-averaged F1-score of 0.80. In the linear mixed-effects model (LMM), the F1-score of automated detection was significantly lower than inter-grader agreement, and this difference was consistently observed regardless of disease status. F1-scores remained stable across the analyzed signal-to-noise ratio (SNR) range.
Conclusions:
The rule-based automated detection method applied to AO-TFI images showed lower performance than inter-grader agreement in both healthy and RP eyes, while demonstrating stable detection performance regardless of disease status. This approach represents a proof-of-concept for the feasibility of quantitative analysis of RPE cells using AO-TFI. Further validation using larger cohorts and independent datasets is warranted.
Translational Relevance:
The automated RPE cell detection method based on AO-TFI evaluated in this study represents an initial step toward standardized quantitative assessment of RPE. With further validation, it may contribute to the development of imaging biomarkers and their application in clinical research.
