Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Jie Lu; Yuxuan Cheng; Farhan E. Hiya; Mengxi Shen; Gissel Herrera; Qinqin Zhang; Giovanni Gregori; Philip J. Rosenfeld; and Ruikang K. Wang

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Author : Jie Lu, Yuxuan Cheng, Farhan E. Hiya, Mengxi Shen, Gissel Herrera, Qinqin Zhang, Giovanni Gregori, Philip J. Rosenfeld, and Ruikang K. Wang Journa Name: BIOMEDICAL OPTICS EXPRESS Country : USA Volume: 15 issue: 1 Year: 2024 Views : 538

Abstract:

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r?=?0.9551, P?