Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
Michael D. Abramoff
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.
The posterior segments of human eyeball, especially the retina and the choroid, are playing important roles in visual processing. The retina is the light-sensitive layer of tissue, which acts like a film in the camera to receive images and convert them to electric signals. The choroid is the layer of blood vessels and connective tissue that supply oxygen and nutrients to the inner parts of the eye including the retina.
Many eye diseases affect these two layers. In this work, we focus on age-related macular degeneration (AMD), a disease that causes a majority of vision loss and blindness among adults over 50 years old. Typically, AMD patients suffer from damages of their central vision and are incapable of seeing objects in the front of them.
AMD-affected eyes are characterized with abnormal cysts and deposits in the retinal and the choroidal regions. Quantifying these abnormalities is crucial in the diagnosis of AMD. Traditionally, doctors used two-dimensional images of the posterior segment of AMD patients eyes to roughly evaluate the conditions of patients eye(s). This type of diagnosis is inefficient and often less accurate, given that it requires a doctor to manually analyze a substantial number of images for hours.
In this work, we use a novel three-dimensional modality of eye imaging – optical coherence tomography (OCT) imaging – to access the delicate structures of human eyes. Furthermore, we develop several advanced and automated image processing algorithms and methods to measure important indices of AMD disease such as the size of cysts and deposits, the area of the affected regions, the volume of the related vessels, and the thickness of the related layers. Using our automated analyses, doctors could obtain the quantitative references with high accuracy by just “one-click". Thus, our methods have the potential to improve the diagnosis and the management of the AMD among other eye diseases.
publicabstract, Algorithm, Automated analysis, Automated segmentation, Image processing, Ophthalmology, Optical coherence tomography
xii, 139 pages
Includes bibliographical references (pages 126-139).
Copyright 2015 Li Zhang