Event Details:
Location
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Assistant Professor
Vanderbuilt University
Title: Medical Image Segmentation and Synthesis
Abstract: Segmentation and synthesis are two fundamental tasks in medical image computing. Segmentation refers to the delineation of the boundaries of a structure of interest in the image, such as an organ, a tumor, or a lesion. Synthesis refers to images created computationally from other data; common examples include cross-modality synthesis and image denoising. This talk will provide an overview of my lab's recent work in these two broad algorithmic directions in the context of a wide range of medical imaging applications. These driving clinical problems include MR imaging of the brain, OCT imaging of the retina, ultrasound imaging of the placenta, and endoscopic imaging of the kidney. I will also illustrate many problem formulations where synthesis can be used to help segmentation, and vice versa.
About: Ipek Oguz is an Associate Professor in the Department of Computer Science at Vanderbilt University, with secondary appointments in Electrical and Computer Engineering and Biomedical Engineering. She received her Ph.D. in Computer Science at the University of North Carolina at Chapel Hill. Prior to joining Vanderbilt, she worked in the Penn Image Computing and Science Laboratory (PICSL) and Center for Biomedical Image Computing and Analytics (CBICA) at the University of Pennsylvania as well as in the Iowa Institute for Biomedical Imaging (IIBI) at the University of Iowa. Her research is in the field of medical image computing and specifically in the development of novel methodology for quantitative medical image analysis, with applications to ophthalmic imaging, obstetric imaging, endoscopic imaging and neuroimaging. Her technical interests include image segmentation, image synthesis and deep learning. She has co-authored more than 200 peer-reviewed journal and conference publications. She was a founding member of the Women in MICCAI Committee, and she is an Associate Editor for the Medical Image Analysis and the Machine Learning for Biomedical Imaging journals. She served as a co-chair of IPMI 2017 and MIDL 2023, and she will be the general chair of IPMI 2025.
Attendance is open to the Stanford and AIMI affiliate community. Please contact aimicenter@stanford.edu for the Zoom link if you would like to attend virtually.