Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos
Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos
复制标题
通过深度视频分类量化多发性硬化症的进展
DOI：
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发表时间：
2014
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通讯作者：
A. Criminisi
中科院分区：
文献类型：
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作者：
P. Kontschieder;J. Dorn;C. Morrison;R. Corish;Darko Zikic;A. Sellen;M. D’Souza;C. Kamm;J. Burggraaff;P. Tewarie;T. Vogel;Michela Azzarito;B. Glocker;P. Chin;F. Dahlke;C. Polman;L. Kappos;B. Uitdehaag;A. Criminisi
本文提出了基于学习的新技术，用于测量多发性硬化症(MS)患者的疾病进展。我们的系统旨在通过增加疾病进展的定量证据来增强传统的神经学检查。在检查过程中，使用现成的深度相机对患者进行成像，要求他/她执行精心挑选的动作。我们的算法然后自动分析视频，评估每个动作的质量，并将它们归类为健康或不健康。我们的贡献有三个方面：i)引入随机化的支持向量机分类器集成，并将它们与决策森林在深度视频分类任务中进行比较；ii)演示深度视频中区分标志的自动选择，显示其临床相关性；iii)在1041个MS患者和健康志愿者的新数据集上定量验证我们的分类算法。我们获得了远远超过80%的平均Dice分数，证实了我们方法在实际应用中的有效性。我们的结果表明，这项技术在深度相机支持的一系列条件下的临床评估中可能是富有成效的。
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.