Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos

  • Peter Kontschieder ,
  • Jonas F. Dorn ,
  • ,
  • Bob Corish ,
  • Darko Zikic ,
  • ,
  • Marcus DSouza ,
  • Christian P. Kamm ,
  • Jessica Burggraaff ,
  • Prejaas Tewarie ,
  • Thomas Vogel ,
  • Michael Azzarito ,
  • Ben Glocker ,
  • Peter Chin ,
  • Frank Dahlke ,
  • Chris Polman ,
  • Ludwig Kappos ,
  • Bernard Uitdehaag ,

MICCAI 2014 - Intl Conf. on Medical Image Computing and Computer Assisted Intervention |

Published by Springer

This paper presents new learning-based techniques for measuring disease progression in Multiple Sclerosis (MS) patients. Our system aims to augment conventional neurological examinations by adding quantitative evidence of disease progression. An o -the-shelf depth camera is used to image the patient at the examination, during which he/she is asked to perform carefully selected movements. Our algorithms then automatically analyze the videos, assessing the quality of each movement and classifying them as healthy or non-healthy. Our contribution is three-fold: We i) introduce ensembles of randomized SVM classi ers and compare them with decision forests on the task of depth video classi cation; ii) demonstrate automatic selection of discriminative landmarks in the depth videos, showing their clinical relevance; iii) validate our classi cation algorithms quantitatively on a new dataset of 1041 videos of both MS patients and healthy volunteers. We achieve average Dice scores well in excess of the 80% mark, con rming the validity of our approach in practical applications. Our results suggest that this technique could be fruitful for depth-camera supported clinical assessments for a range of conditions.