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Constrained manifold learning for the characterization of pathological deviations from normality

机译：约束流形学习用于表征与正常的病理偏离

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摘要

This paper describes a technique to (1) learn the representation of a pathological motion pattern from a given population, and (2) compare individuals to this population. Our hypothesis is that this pattern can be modeled as a deviation from normal motion by means of non-linear embedding techniques. Each subject is represented by a 2D map of local motion abnormalities, obtained from a statistical atlas of myocardial motion built from a healthy population. The algorithm estimates a manifold from a set of patients with varying degrees of the same disease, and compares individuals to the training population using a mapping to the manifold and a distance to normality along the manifold. The approach extends recent manifold learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Interpolation techniques using locally adjustable kernel improve the accuracy of the method. The technique is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssynchrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 37 CRT candidates and 21 healthy volunteers. Experiments highlight the relevance of non-linear techniques to model a pathological pattern from the training set and compare new individuals to this pattern.
机译：本文介绍了一种技术（1）从给定的种群中学习病理运动模式的表示，以及（2）将个体与该种群进行比较。我们的假设是可以通过非线性嵌入技术将这种模式建模为与正常运动的偏差。每个受试者均由从健康人群构建的心肌运动统计图集获得的局部运动异常二维图表示。该算法从一组具有不同程度的同一疾病的患者中估计出一个歧管，并使用到该歧管的映射以及沿该歧管的正常距离将个体与训练人群进行比较。该方法通过限制歧管经过代表正常运动模式的生理学有意义的起点，从而扩展了最新的歧管学习技术。使用局部可调核的插值技术提高了该方法的准确性。该技术在心脏再同步治疗（CRT）的背景下应用，重点是称为间隔闪光（SF）的心室内不同步的特定运动模式。我们从50名具有SF的CRT候选者中估算出歧管，并在37名CRT候选者和21名健康志愿者中进行了测试。实验强调了非线性技术对训练集中的病理模式进行建模并将新个体与该模式进行比较的相关性。

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