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Updated: Mar 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence
Maribel Hidalgo-Aguirre1, Julian Gitelman2, Mark Richard Lesk3
1Institut National de la Recherche Scientifique Centre Energie, Materiaux et Telecommunications, 1650 Boulevard Lionel-Boulet, Varennes, Québec J3X 1S2, CanadabMaisonneuve-Rosemont Hospital, Research Center, 5415 L'Assomption, Montreal, QC H1T 2M4, Canada.
Abstract:
Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

