This Career Development Award-2 (CDA-2) is for supporting an early-career engineering faculty member for three years. It will provide the means to train a young researcher currently possessing broad image analysis experience and narrowly focused expertise in one ophthalmic imaging modality (optical coherence tomography, OCT) in a multitude of ophthalmic modalities and clinical areas, with a special focus on glaucoma. While the nominee has already demonstrated success as a member of interdisciplinary teams with VA clinicians and is an investigator associated with the Iowa City VA Center of Excellence for the Prevention and Treatment of Visual Loss, this CDA is specifically designed to enhance her ophthalmic clinical knowledge to enable her to develop into an ophthalmic image analysis expert with an unprecedented level of clinical understanding for an engineering-trained scholar and with the ability to contribute substantially to interdisciplinary translational and clinically oriented ophthalmic research of interest to veterans. Because of the current variability associated with standard functional measurements for the diagnosis and assessment of glaucoma, the research plan is designed to work towards obtaining better structural parameters to enable (1) improved diagnostic capabilities, (2) an objective basis for disease staging, and (3) an improved ability to measure disease progression. More specifically, with the overall hypothesis that the multimodal combination of information from stereo fundus photography and spectral-domain OCT (SD-OCT) will enable the discovery of less variable (across normal subjects) and more reproducible structural parameters relevant to the diagnosis, management, or understanding of glaucoma, the plan addresses the following specific aims:
7 Aim 1 : Develop the novel multimodal methodology for obtaining less variable layer-thickness and more reproducible optic-nerve-head structural parameters for the assessment of glaucoma. - Aim 1a: Develop the methodology for simultaneously segmenting the blood vessels in fundus photographs and SD-OCT volumes. Refine the methodology for correcting the retinal nerve fiber layer thickness/volume and ganglion cell layer thickness/volume in SD-OCT volumes based on the presence of blood vessels derived from SD-OCT and/or fundus photography. Verify that such a correction results in a lower variability in regional thickness measurements across normal subjects. - Aim 1b: Develop the methodology for simultaneously segmenting the optic disc, neural canal opening, and cup in SD-OCT images and stereo fundus photographs. Compute the reproducibility of such measurements in normal eyes and in eyes with glaucoma.
7 Aim 2 : Characterize the relationship between the inner layers of the retina and the outer layers of the retina in normal eyes and in eyes varying in refractive error, axial eye length, and age. Determine whether this relationship will enable reduced variability of inner retinal thickness measurements across normal subjects and better correlation of thickness parameters with disease status in glaucoma subjects.
7 Aim 3 : Relate the thickness/volume of the inner retinal layer containing ganglion cells (corrected and uncorrected based on the presence of vessels) to a) the retinal nerve fiber layer thickness (corrected and uncorrected based on the presence of vessels) and b) the cross-sectional area of the rim tissue at the plane of the neural canal opening in normal and glaucoma subjects. In addition to its direct relevance to glaucoma, the image analysis methodology developed as part of the research plan will be relevant to telemedical applications and other ophthalmic (and systemic) diseases. This will thus enable further benefit for the veteran population and will set the stage for future research in these areas by the nominee.
The career development plan and research plan described in this proposal will contribute towards the engineering-trained candidate's career goal of utilizing image analysis techniques to aid in the diagnosis, understanding, and management of ophthalmic diseases causing blindness, with a special focus on glaucoma.
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|Miri, Mohammad Saleh; Abràmoff, Michael D; Lee, Kyungmoo et al. (2015) Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach. IEEE Trans Med Imaging 34:1854-66|
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|Antony, Bhavna J; Abràmoff, Michael D; Harper, Matthew M et al. (2013) A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes. Biomed Opt Express 4:2712-28|