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Quantifying progression of multiple sclerosis via classification of depth videos.
- 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.
- Subjects :
- Artificial Intelligence
Disease Progression
Humans
Image Interpretation, Computer-Assisted methods
Movement Disorders etiology
Multiple Sclerosis complications
Reproducibility of Results
Sensitivity and Specificity
Diagnostic Techniques, Neurological
Imaging, Three-Dimensional methods
Movement Disorders diagnosis
Multiple Sclerosis diagnosis
Pattern Recognition, Automated methods
Video Recording methods
Whole Body Imaging methods
Subjects
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