2026-10-12T14:04:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32641260 2026-10-01T16:24:37Z portal_1172 item_type_8 month_year_10_2026

Developing spatial models for early glaucoma detection using retinal ganglion cell point patterns Jonathan Henderson (24169902) PUREID: 641308859 Spatial point processes approximate Bayesian computation change point detection neurodegenerative disease glaucoma fractal characterisations point pattern log-Gaussian Cox process Strauss process Glaucoma is a neurodegenerative disease that affects millions worldwide and often leads to irreversible vision loss. One of the main challenges in managing glaucoma is its delayed diagnosis, during which early, subtle changes in disease progression may go unnoticed. This research aims to establish sensitive and robust spatial measures for early detection and use them to model disease progression. Glaucoma leads to the degeneration of retinal ganglion cells (RGCs), which transmit visual information from the eye to the brain. Their loss is irreversible and results in blindness. Current treatments focus on preventing further degeneration, while research seeks to understand the mechanisms driving RGC loss. This study uses image data from a well-established rodent model of glaucoma. Although earlier studies applied non-parametric methods to identify differences between healthy and diseased tissue, these approaches struggle with the complexity of the data, which includes biological noise, variability across samples, and differing cellular interactions. To address these challenges, this work develops point pattern analysis methods that incorporate fractal measures, which are more robust to noise, and second-order statistics to capture subtle structural changes in cell distribution. A model is constructed using approximate Bayesian computation (ABC), integrating aspects of the log-Gaussian Cox and Strauss processes. It is extended to include marks, spatial covariates, and replicate structures, and is used alongside change point detection to identify spatial deviations from healthy patterns. The findings reveal retinal regions that may be more susceptible to RGC loss, offering insights into disease progression and enabling more targeted diagnostic focus. This approach advances spatial modelling methods and provides a promising framework for early glaucoma detection, with potential clinical applications for improving diagnosis and treatment outcomes. 2026-10-01T16:24:37Z Text Thesis 10.17034/32641260.v1 https://figshare.com/articles/thesis/Developing_spatial_models_for_early_glaucoma_detection_using_retinal_ganglion_cell_point_patterns/32641260 All Rights Reserved