title: Quantifying progression of multiple sclerosis via classification of depth videos.
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.
authors.forename: Peter
authors.initials: P
authors.lastname: Kontschieder
authors.forename: Jonas F
authors.initials: JF
authors.lastname: Dorn
authors.forename: Cecily
authors.initials: C
authors.lastname: Morrison
authors.forename: Robert
authors.initials: R
authors.lastname: Corish
authors.forename: Darko
authors.initials: D
authors.lastname: Zikic
authors.forename: Abigail
authors.initials: A
authors.lastname: Sellen
authors.forename: Marcus
authors.initials: M
authors.lastname: D'Souza
authors.forename: Christian P
authors.initials: CP
authors.lastname: Kamm
authors.forename: Jessica
authors.initials: J
authors.lastname: Burggraaff
authors.forename: Prejaas
authors.initials: P
authors.lastname: Tewarie
authors.forename: Thomas
authors.initials: T
authors.lastname: Vogel
authors.forename: Michela
authors.initials: M
authors.lastname: Azzarito
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Peter
authors.initials: P
authors.lastname: Chin
authors.forename: Frank
authors.initials: F
authors.lastname: Dahlke
authors.forename: Chris
authors.initials: C
authors.lastname: Polman
authors.forename: Ludwig
authors.initials: L
authors.lastname: Kappos
authors.forename: Bernard
authors.initials: B
authors.lastname: Uitdehaag
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
mesh_terms: D001185:Artificial Intelligence; D003943:Diagnostic Techniques, Neurological; D018450:Disease Progression; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009069:Movement Disorders / Q000175:diagnosis* / Q000209:etiology; D009103:Multiple Sclerosis / Q000150:complications / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*