Please join the Goergen Institute for Data Science for Machine Learning and Computational Imaging for Optimal Utilization of Biophotonic Imaging Systems in Laboratory and Clinical Trials, a research seminar with Sina Farsiu, Associate Professor of Biomedical Engineering and Opthamology and Director of the Vision and Image Processing Lab at Duke University.
Abstract: While the performance of biomedical image segmentation algorithms has been significantly improved in recent years, a critical question has not yet been addressed: "How far is segmentation performance from its theoretical limit?" The answer to this question justifies further investment of time and financial resources to gain higher segmentation accuracy. In this talk, we review optical, mathematical, and biological factors that limit accurate quantification of functional and anatomical features in biomedical optics applications. We introduce machine learning and computational imaging software and hardware to enhance the resolution and the accuracy of quantifying imaging biomarkers of disease in various laboratory and clinical applications.
Bio: Sina Farsiu received his Ph.D. in electrical engineering from the University of California, Santa Cruz in 2005. He is currently the Paul Ruffin Scarborough Associate Professor of Engineering and the director of the Vision and Image Processing Laboratory in the Departments of Biomedical Engineering and Ophthalmology, with secondary appointments in the Departments of Electrical and Computer Engineering and Computer Science at Duke University. Dr. Farsiu has served as a Senior Area Editor for the IEEE Transactions on Image Processing, an Associate Editor of Biomedical Optics Express, an Associate Editor of SIAM Journal on Imaging Sciences, and is a program committee member of SPIE Ophthalmic Technologies and the Association for Research in Vision and Ophthalmology (ARVO) Imaging in the Eye conferences. He is a recipient of ARVO Foundation/Pfizer Ophthalmics Carl Camras Translational Research Award. He is a Fellow of IEEE, SPIE, OSA, and AIMBE.
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Meeting ID: 980 2212 6856
Friday, December 4 at 2:00pm to 3:00pmVirtual Event