内容紹介

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 make use of the inherent correlation and causality of repeated acquisitions. We will discuss crucial aspects of longitudinal imaging such as image harmonization, image curation and synthesis, and longitudinal modeling and segmentation, driven by ongoing clinical studies related to analysis of early brain growth in subjects at risk for autism, analysis of neurodgeneration in Huntington's disease, and quantitative assessment of progression of glaucoma from OCT imaging. 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　We will explain this context, why a monotone operator is useful, how to train a deep network to remain monotone, and applications in blind deconvolution.

詳細情報

刊年

2024

形態

電子化映像資料(1時間28分43秒）

シリーズ名

情報科学領域・コロキアム ; 2023年度

注記

講演者所属: New York University

講演日: 2024年3月28日

講演場所: エーアイ大講義室, AI Inc. Seminar Hall （L1）

標題言語

英語 (eng)

本文言語

英語 (eng)

著者情報

Guido Gerig