TalkMartinReuter

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Invited Talk by Prof. Dr. Martin Reuter

Dr Martin Reuter

Prof. Dr. Martin Reuter
Director of Image Analysis at the German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Assistant Professor of Radiology, Harvard Medical School
Assistant Professor of Neurology, Harvard Medical School

  • Date: Wednesday, August 2, 2017
  • Time: 10:30 - 12:00
  • Location: FMI 00.13.009A (MI Building, Campus Garching), Boltzmannstr. 3, Garching b. München


Talk Overview

Title: Sensitive Longitudinal NeuroImaging? and Morphometry

Abstract: Structural magnetic resonance imaging data are frequently analyzed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this talk we will describe sensitive computational methods for the automated quantification of neuroanatomical morphometric estimates and their longitudinal changes. We will, furthermore, show that neuroanatomical shape features, such as lateral shape asymmetry, demonstrate increased sensitivity to detect early disease effects and can improve computer-aided diagnosis of dementia.

Biography: Prof. Reuter is Director of Image Analysis at the German Center for Neurodegenerative Diseases (DZNE) in Bonn. He is Assistant Professor of Radiology and Assistant Professor of Neurology at the Harvard Medical School in Boston, USA. He, furthermore, directs the Laboratory for Computational Longitudinal Neuroimaging at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and is Research Affiliate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology. Prof. Reuter obtained a degree in Mathematics and Computer Science in 2001 and a Ph.D. in Computational Geometry in 2005 from the Leibniz University Hanover, Germany. He pursued his postdoctoral studies with a Feodor Lynen Fellowship of the Alexander von Humboldt Foundation at the Massachusetts Institute of Technology until 2008 and subsequently moved to the Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. He has received prizes and awards from international journals, workshops, competitions, industry and funding bodies, including a competitive career award of the National Institute of Health (NIH). Prof. Reuter’s research contributions include advanced non-rigid shape analysis and highly sensitive computational methods for longitudinal neuroimaging and computer-aided diagnosis. His methods are widely employed as part of the FreeSurfer? software suite, for example, to uncover risk and preserving factors of neurodegenerative diseases or to assess disease modifying therapies. Furthermore, various neuroimaging studies, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), rely on his contributions for the automated quantification of relevant biomarkers from large imaging data sets.

WebEventForm
Title: Talk on Sensitive Longitudinal NeuroImaging? and Morphometry
Date: 2 August 2017
Location: FMI 00.13.009A
Abstract: Structural magnetic resonance imaging data are frequently analyzed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this talk we will describe sensitive computational methods for the automated quantification of neuroanatomical morphometric estimates and their longitudinal changes. We will, furthermore, show that neuroanatomical shape features, such as lateral shape asymmetry, demonstrate increased sensitivity to detect early disease effects and can improve computer-aided diagnosis of dementia.
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