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SUMMARY:Learning and Discovery of Clinically Useful Information from Medic
 al Images - Daniel Rueckert\, Imperial College London
DTSTART:20120426T100000Z
DTEND:20120426T110000Z
UID:TALK37287@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Three-dimensional (3D) and four-dimensional (4D) imaging plays
  an increasingly important role in computer-assisted diagnosis\, intervent
 ion and therapy. However\, in many cases the interpretation of these image
 s is heavily dependent on the subjective assessment of the imaging data by
  clinicians. Over the last decades image registration has transformed the 
 clinical workflow in many areas of medical imaging. At the same time\, adv
 ances in machine learning have transformed many of the classical problems 
 in computer vision into machine learning problems. This talk will focus on
  the convergence of image registration and machine learning techniques for
  the discovery and quantification of clinically useful information from me
 dical images. To illustrate this I will show several examples such as the 
 segmentation of neuro-anatomical structures\, the discovery of biomarkers 
 for neurodegenerative diseases such as Alzheimer’s and the quantificatio
 n of temporal changes such as growth in the d eveloping brain.
LOCATION:Small lecture theatre\, Microsoft Research Ltd\, 7 J J Thomson Av
 enue (Off Madingley Road)\, Cambridge
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