Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos
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.
KeywordsMultiple Sclerosis Random Forest Expand Disability Status Scale Depth Camera Depth Video
Unable to display preview. Download preview PDF.
- 1.MICCAI Workshop on Medical Image Analysis on Multiple Sclerosis: validation and methodological issues (MIAMS) (2009)Google Scholar
- 2.Bradski, G.: Opencv. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
- 4.Criminisi, A., Shotton, J.: Decision Forests in Computer Vision and Medical Image Analysis. Springer (2013)Google Scholar
- 6.Datta, S., Narayana, P.A.: A comprehensive approach to the segmentation of multichannel three-dimensional mr brain images in multiple sclerosis. PAMI 2 (2013)Google Scholar
- 7.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)Google Scholar
- 9.Kurtzke, J.F.: Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 33(11) (1983)Google Scholar
- 10.Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)Google Scholar
- 11.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)Google Scholar
- 12.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)Google Scholar
- 13.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)Google Scholar
- 15.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)Google Scholar