Optical coherence tomography (OCT) is becoming an increasingly important modality for the diagnosis and management of a variety of eye diseases, such as age-related macular degeneration (AMD), glaucoma, and diabetic macular edema (DME). Spectral domain OCT (SD-OCT), an advanced type of OCT, produces three dimensional high-resolution cross-sectional images and demonstrates delicate structure of the functional portion of posterior eye, including retina, choroid, and optic nerve head. As the clinical importance of OCT for retinal disease management and the need for quantitative and objective disease biomarkers grows, fully automated three-dimensional analysis of the retina has become desirable. Previously, our group has developed the Iowa Reference Algorithms (http://www.biomed-imaging.uiowa.edu/downloads/), a set of fully automated 3D segmentation algorithms for the analysis of retinal layer structures in subjects without retinal disease. This is the first method of segmenting and quantifying individual layers of the retina in three dimensions. However, in retinal disease, the normal architecture of the retina - specifically the outer retina - is disrupted. Fluid and deposits can accumulate, and normal tissue can be replaced by scar tissue. These abnormalities increase the irregularity of the retinal structure and make quantitative analysis in the image data extra challenging.
In this work, we focus on the segmentation of the retina of patients with age-related macular degeneration, the most important cause of blindness and visual loss in the developed world. Though early and intermediate AMD results in some vision loss, the most devastating vision loss occurs in the two endstages of the disease, called geographic atrophy (GA) respectively choroidal neovascularization (CNV). In GA, because of pathological changes that are not fully understood, the retinal pigment epithelium disappears and photoreceptors lose this supporting tissue and degenerate. Second, in CNV, the growth of abnormal blood vessels originating from the choroidal vasculature causes fluid to enter the surrounding retina, causing disruption of the tissues and eventual visual loss. The severity and progress of early AMD is characterized by the formation of drusen and subretinal drusenoid deposits, structures containing photoreceptor metabolites - primarily lipofuscin - the more drusen the more severe the disease and the higher the risk of progressing to GAD or CNV. Thus, to improve the image guided management of AMD, we will study automated methods for segmenting and quantifying these intraretinal, subretinal and choroidal structures, including different types of abnormalities and layers, focusing on the outer retina.
The major contributions of this thesis include: 1) developing an automated method of segmenting the choroid and quantifying the choroidal thickness in 3D OCT images; 2) developing an automated method of quantifying the presence of drusen in early and intermediate AMD; 3) developing an method of identifying the different ocular structures simultaneously; 4) studying the relationship among intraretinal, subretinal and choroidal structures.