Deep learning method for semi-automated segmentation of optic nerve head tissues in optical coherence tomography images.

ABSTRACT: Optical coherence tomography (OCT) enables acquisition of image volumes of the optic nerve head (ONH) in vivo. These image volumes provide structural and biomechanical information about the ONH. The individual tissues in these images must be segmented to obtain tissue-specific information, but doing so manually is time consuming. We present a method which uses a deep learning model to semi-automatically segment the following tissue boundaries of the ONH: the anterior lamina cribrosa surface (ALCS), Bruch's membrane (BM), and choroid-scleral interface (CS). We trained a convolutional neural network (CNN) to predict tissue boundary locations using 46 pre-processed ONH image volumes consisting of 24 radial scans each and their corresponding manual segmentations. When the trained model is pres

SUBMITTER: Clingo KA 

PROVIDER: S-EPMC12820890 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature