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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
MICCAI Workshop on Medical Image Analysis on Multiple Sclerosis: validation and methodological issues (MIAMS) (2009)
Bradski, G.: Opencv. Dr. Dobb’s Journal of Software Tools (2000)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Criminisi, A., Shotton, J.: Decision Forests in Computer Vision and Medical Image Analysis. Springer (2013)
Criminisi, A., Sharp, T., Blake, A.: GeoS: Geodesic image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 99–112. Springer, Heidelberg (2008)
Datta, S., Narayana, P.A.: A comprehensive approach to the segmentation of multichannel three-dimensional mr brain images in multiple sclerosis. PAMI 2 (2013)
Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance. Neuroimage (2011)
Goodkin, D., Cookfair, D., Wende, K., Bourdette, D., Pullicino, P., Scherokman, B., Whitham, R.: Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke expanded disability status scale (EDSS). Neurology 42(4), 859–859 (1992)
Kurtzke, J.F.: Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 33(11) (1983)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
Noseworthy, J.H., Vandervoort, M.K., Wong, C.J., Ebers, G.C.: Interrater variability with the expanded disability status scale (EDSS) and functional systems (FS) in a multiple sclerosis clinical trial. Neurology 40(6) (1990)
Pfueller, C., Otte, K., Mansow-Model, S., Paul, F., Brandt, A.: Kinect-based analysis of posture, gait and coordination in multiple sclerosis patients. Neurology 80 (2013)
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. PAMI (2013)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)
Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: Proc. DAGM Symposium (DAGM), pp. 214–223 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kontschieder, P. et al. (2014). Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)