Glaucoma is a leading cause of blindness throughout the world and is estimated to affect 80 million by 2020. This disease causes progressive loss of vision and, left untreated, can lead to complete blindness. With treatment, however, disease progression can be slowed dramatically. This makes early detection and intervention crucial in preserving the vision of affected individuals.
Onset and progression of glaucoma are associated with structural changes to an anatomical feature known as the optic nerve head (ONH). The ONH is the site of attachment between the retina and the optic nerve that carries all visual information to the brain. As glaucoma progresses, characteristic changes related to cell death and loss of vision can be observed in the three-dimensional structure of the ONH. A common modality used to observe these changes is stereo fundus imaging. This modality captures three-dimensional information via stereo imaging and is commonly used in clinical settings to diagnose and monitor glaucoma. A limitation of using stereo fundus images is the need for review by glaucoma specialists to identify disease related features of ONH structure. Further, even when expert evaluation is possible, the subjective nature of the process can lead due large discrepancies in the evaluations and resultant clinical decisions. The work presented here seeks address these concerns by providing automated, computational tools that can be used to characterize ONH structure.
Specifically, this thesis outlines the development of computational methods for inferring three-dimensional information from stereo fundus images and identifying objective, quantitative measurements of ONH structure. The resulting computational tools were applied to image and clinical data collected from a large cohort of individuals to identify hidden relationships between ONH structure, clinical measurements, and glaucoma. These tools were then applied to develop methods for estimating the impact of individual genetic factors on the ONH. Finally, using a longitudinal dataset collected over more than a decade, computational analysis was used to investigate how ONH structure changes over time in response to aging, other disease-related factors, and glaucoma progression.