BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Xavier Pennec (INRIA) DTSTART:20220712T073000Z DTEND:20220712T083000Z DTSTAMP:20260423T010620Z UID:GaML/5 DESCRIPTION:Title: Geo metric Statistics for Computational Anatomy\nby Xavier Pennec (INRIA) as part of Workshop on Geometry and Machine Learning\n\n\nAbstract\nAt the interface of geometry\, statistics\, image analysis and medicine\, comput ational anatomy aims at analysing and modelling the biological variability of the organs shapes and their dynamics at the population level. The goal is to model the mean anatomy\, its normal variation\, its motion / evolut ion and to discover morphological differences between normal and pathologi cal groups. However\, shapes are usually described by equivalence classes of sets of points\, curves\, surfaces or images under the action of a tran sformation group\, or directly by the diffeomorphic deformation of a templ ate in diffeomorphometry. This implies that they live in non-linear spaces \, while statistics where essentially developed in a Euclidean framework. For instance\, adding or subtracting curves or surfaces does not really ma ke sense. Thus\, there is a need for redefining a consistent statistical f ramework for objects living in manifolds and Lie groups\, a field which is now called geometric statistics. The objective of this talk is to give an overview of the Riemannian computational tools and of simple statistics i n these spaces. The talk is motivated and illustrated by applications in m edical image analysis\, such as the regression of simple and efficient mod els of the atrophy of the brain in Alzheimer’s disease and the groupwise analysis of the motion of the heart in sequences of images using the para llel transport of surface and image deformations.\n LOCATION:https://researchseminars.org/talk/GaML/5/ END:VEVENT END:VCALENDAR