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Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos

  • Peter Kontschieder
  • Jonas F. Dorn
  • Cecily Morrison
  • Robert Corish
  • Darko Zikic
  • Abigail Sellen
  • Marcus D’Souza
  • Christian P. Kamm
  • Jessica Burggraaff
  • Prejaas Tewarie
  • Thomas Vogel
  • Michela Azzarito
  • Ben Glocker
  • Peter Chin
  • Frank Dahlke
  • Chris Polman
  • Ludwig Kappos
  • Bernard Uitdehaag
  • Antonio Criminisi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

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 off-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 classifiers and compare them with decision forests on the task of depth video classification; ii) demonstrate automatic selection of discriminative landmarks in the depth videos, showing their clinical relevance; iii) validate our classification 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, confirming 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.

Keywords

Multiple Sclerosis Random Forest Expand Disability Status Scale Depth Camera Depth Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Peter Kontschieder
    • 1
  • Jonas F. Dorn
    • 2
  • Cecily Morrison
    • 1
  • Robert Corish
    • 1
  • Darko Zikic
    • 1
  • Abigail Sellen
    • 1
  • Marcus D’Souza
    • 3
  • Christian P. Kamm
    • 4
  • Jessica Burggraaff
    • 5
  • Prejaas Tewarie
    • 5
  • Thomas Vogel
    • 2
  • Michela Azzarito
    • 2
  • Ben Glocker
    • 1
  • Peter Chin
    • 6
  • Frank Dahlke
    • 2
  • Chris Polman
    • 5
  • Ludwig Kappos
    • 3
  • Bernard Uitdehaag
    • 5
  • Antonio Criminisi
    • 1
  1. 1.Microsoft ResearchUK
  2. 2.Novartis PharmaSwitzerland
  3. 3.University Hospital BaselSwitzerland
  4. 4.University Hospital BernSwitzerland
  5. 5.VU University Medical Center AmsterdamThe Netherlands
  6. 6.Novartis Pharmaceuticals East HanoverUSA

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