DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images.

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

Purpose: To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH).

Methods: A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation.

In Vivo 3-Dimensional Strain Mapping of the Optic Nerve Head Following Intraocular Pressure Lowering by Trabeculectomy.

Purpose: To map the 3-dimensional (3D) strain of the optic nerve head (ONH) in vivo after intraocular pressure (IOP) lowering by trabeculectomy (TE) and to establish associations between ONH strain and retinal sensitivity.

Design: Observational case series.

Participants: Nine patients with primary open-angle glaucoma (POAG) and 3 normal controls.