Automated retinal layer segmentation in mouse OCT using deep learning and graph search

Optical coherence tomography (OCT), a non-invasive imaging technique, produces cross-sectional information of the retina and quantitative measurements of the retinal structural changes, which provide valuable insights into disease progression. Traditionally, retinal layer thicknesses in mice are measured using manual caliper tracing on OCT displays. While manual tracing can be effective, it is time-consuming, prone to human errors, and often shows intra-observer and inter-observer variability. This thesis seeks to overcome these limitations by evaluating a two-step three-dimensional deep learning graph-search (DLGS) approach, that was originally designed to segment retinal layers in human macular OCT scans, even in cases of severe retinal ganglion cell layer complex (RGC) thinning, for mouse OCT.