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.
Original language | English |
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Title of host publication | Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings |
Publisher | Springer |
Pages | 429-437 |
Number of pages | 9 |
Volume | 17 |
Edition | PART 2 |
ISBN (Print) | 9783319104690 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, United States Duration: 14 Sept 2014 → 18 Sept 2014 Conference number: 17 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 2 |
Volume | 8674 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 |
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Abbreviated title | MICCAI 2014 |
Country/Territory | United States |
City | Boston |
Period | 14/09/14 → 18/09/14 |
Keywords
- n/a OA procedure