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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer
Pages429-437
Number of pages9
Volume17
EditionPART 2
ISBN (Print)9783319104690
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 14 Sep 201418 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8674 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Country/TerritoryUnited States
CityBoston, MA
Period14/09/1418/09/14

Keywords

  • n/a OA procedure

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