Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos - Kontschieder, Peter; Dorn, Jonas F.; Morrison, Cecily; Corish, Bob; Zikic, Darko; Sellen, Abigail; DSouza, Marcus; Kamm, Christian P.; Burggraaff, Jessica; Tewarie, Prejaas; Vogel, Thomas; Azzarito, Michael; et al. (MICCAI 2014 - Intl Conf. on Medical Image Computing and Computer Assisted Intervention, 2014)
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 o -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 classi ers and compare them with decision forests on the task of depth video classi cation; ii) demonstrate automatic selection of discriminative landmarks in the depth videos, showing their clinical relevance; iii) validate our classi cation 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, con rming 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.