Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

Abstract

Purpose: To develop and validate a deep learning model to automatically segment three structures using an anterior segment optical coherence tomography (AS-OCT): The intraocular lens (IOL), the retrolental space (IOL to the posterior lens capsule) and Berger's space (BS; posterior capsule to the anterior hyaloid membrane).

Methods: An artificial intelligence (AI) approach based on a deep learning model to automatically segment the IOL, the retrolental space, and BS in AS-OCT, was trained using annotations from an experienced clinician. The training, validation and test set consisted of 92 cross-sectional OCT slices, acquired in 47 visits from 41 eyes. Annotations from a second experienced clinician in the test set were additionally evaluated to conduct an inter-reader variability analysis.

Results: The AI model achieved a Precision/Recall/Dice score of 0.97/0.90/0.93 for IOL, 0.54/0.65/0.55 for retrolental space, and 0.72/0.58/0.59 for BS. For inter-reader variability, Precision/Recall/Dice values were 0.98/0.98/0.98 for IOL, 0.74/0.59/0.62 for retrolental space, and 0.58/0.57/0.57 for BS. No statistical differences were observed between the automated algorithm and the inter-reader variability for BS segmentation.

Conclusion: The deep learning model allows for fully automatic segmentation of all investigated structures, achieving human-level performance in BS segmentation. We, therefore, expect promising applications of the algorithm with particular interest in BS in automated big data analysis and real-time intra-operative support in ophthalmology, particularly in conjunction with primary posterior capsulotomy in femtosecond laser-assisted cataract surgery.

Keywords: Berger space; IOL; OCT; PPLC; artificial intelligence; cataract surgery; deep learning; femtosecond laser; retrolental space.