Time Will Tell: Longitudinal Image Analysis to Meet Clinical Needs
Guido Gerig
November 11, 2020, Wednesday, 3:00 PM - 4:00 PM EDT
Abstract
Clinical assessment routinely uses terms such as development, growth trajectory, aging, degeneration, disease progress, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that measurement of dynamic spatiotemporal changes may provide information not available from single snapshots in time. Image processing of temporal series of 3-D data embedding time-varying anatomical objects and functional measures requires a new class of analysis methods and tools that makes use of the inherent correlation and causality of repeated acquisitions. This talk will discuss progress in the development of advanced 4-D image and shape analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. We will demonstrate that statistical concepts of longitudinal data analysis such as linear and nonlinear mixed-effect modeling, commonly applied to univariate or low-dimensional data, can be extended to structures and shapes modeled from longitudinal image data, ranging from modeling of changes of shape, image contrast up to ODFs in diffusion MRI. Most relevant to clinical studies, we will also cover inclusion of subject's covariates such as sex and diagnostic scores, into longitudinal image and shape analysis.

The rapidly increasing role of learning-based techniques, enabled by the availability of large publicly shared image databases, will be discussed related to crucial aspects of longitudinal imaging such as image harmonization, automated quality control and correction, and image curation and synthesis. We will discuss results from ongoing clinical studies such as analysis of early brain growth in controls and subjects at risk for autism, analysis of neurodgeneration in normal aging and Huntington's disease, and quantitative assessment of progression of glaucoma.
Bio
Guido Gerig is Department Chair and Institute Professor at the NYU Tandon School of Engineering in the Department of Computer Science and Engineering. He also holds associated/affiliated appointments at NYU Courant CS, NYU Langone Child and Adolescent Psychiatry, Psychiatry and Radiology.

Guido Gerig has been named IEEE Fellow (class of 2019) and has also been appointed as a Fellow of the American Institute for Medical and Biological Engineering (AIMBE) in 2010.

Starting image analysis with applications to satellite imaging, he became increasingly interested in driving problems from medicine, tackled in close multidisciplinary collaboration between medicine, engineering, and statistics. His research supports a number of clinical imaging research studies with novel, innovative image analysis methodologies related to segmentation, registration, atlas building, shape analysis, and image statistics. Driving clinical problems include research in autism, Down's syncdrome, eye diseases (Glaucoma, AMD), multiple sclerosis, Huntington's disease, studies of infants at risk for mental illness, and in general analysis of anatomical changes from normal due to disease, therapy and recovery. Gerig's research resulted in various new image analysis methodologies for nonlinear processing, multi-scale segmentation and shape analysis, some of them first and seminal to the field. Applications resulted in new clinical research discoveries such as vulnerability for schizophrenia, early diagnosis of autism based on differences in brain development trajectories, and correlation of shape atrophy with risk status in Huntington's. New tools and methods are developed as open source software and made available to the public, including teaching materials and hands-on training workshops.

Guido Gerig was previously USTAR Professor of Computer Science at the University of Utah (2007-2015) establishing the Utah Center for Neuroimage Analysis (UCNIA), Taylor Grandy Professor of Computer Science and Psychiatry at the University of North Carolina at Chapel Hill (1998-2007) launching the UNC Neuro Image Research and Analysis Laboratories (NIRAL), and Assistant Professor at ETH Zurich (1993-1998). Gerig holds several awards from Utah and UNC for Excellence in Teaching.