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Quantifying progression of multiple sclerosis via classification of depth videos.

Authors :
Kontschieder P
Dorn JF
Morrison C
Corish R
Zikic D
Sellen A
D'Souza M
Kamm CP
Burggraaff J
Tewarie P
Vogel T
Azzarito M
Glocker B
Chin P
Dahlke F
Polman C
Kappos L
Uitdehaag B
Criminisi A
Source :
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2014; Vol. 17 (Pt 2), pp. 429-37.
Publication Year :
2014

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.

Details

Language :
English
Volume :
17
Issue :
Pt 2
Database :
MEDLINE
Journal :
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Publication Type :
Academic Journal
Accession number :
25485408
Full Text :
https://doi.org/10.1007/978-3-319-10470-6_54