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Macular Degeneration Research - Current Award

Photo Pending

Sina Farsiu, Ph.D.

Duke University
Durham, NC

Title: Automatic Measurement of Wet AMD's Imaging Biomarkers
Non-Technical Title: Automatic Detection and Measurement of neovascular AMD's Imaging Biomarkers

Co-Investigator(s):
Glenn Jaffe, Duke University

Duration: April 1, 2010 - March 31, 2012
Award Type: Standard
Award Amount: $100,000


Summary:

We are developing an open source fully-automated software program with demonstrated high accuracy that is able to detect, segment, and analyze Neovascular AMD (NVAMD) pathology seen on optical coherence tomography and compare these data to corresponding features on other imaging modalities. We anticipate that the software tools developed in this proposal will be readily adopted by clinicians, clinical study sites, and image Reading Centers to better identify NVAMD at the earliest stages, to quantify disease progression, and to measure response to therapy.

Details:

Progress Updates:

We are developing a fully-automated software program with demonstrated high accuracy that is able to detect, segment, and analyze neovascular AMD (NVAMD) pathology seen on spectral domain optical coherence tomography (SDOCT) and compare these data to corresponding features on other imaging modalities.

So far, we have achieved significant progress on all aims of this project, often exceeding our proposed project timeline. Moreover, our experience in this project helped us to achieve exciting progress in related dry-AMD projects. We have submitted two articles on automatic segmentation of normal and AMD eyes to the most prestigious journals of our field (one already published and one under review). Moreover, one of our abstracts received the National Eye Institute’s travel award at the Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, May 2011. The automated segmentation technology that we have developed for AMD eyes, has also resulted in a serendipitous discovery of a novel technique for automatic segmentation of corneal images, resulting in yet another submission of a journal article.

Overall, in part based on our work in this project, we have submitted 5 journal papers. Based on the year one results, we are confident to attain all our major goals based on the proposed timeline. We anticipate that the software tools developed in this proposal will be readily adopted by clinicians, clinical study sites, and image Reading Centers to better identify NVAMD at the earliest stages, to quantify disease progression, and to measure response to therapy.