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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011

Volume 6893 of the series Lecture Notes in Computer Science pp 256-263

Characterizing Pathological Deviations from Normality Using Constrained Manifold-Learning

  • Nicolas DuchateauAffiliated withLancaster UniversityCenter for Computational Imaging & Simulation Technologies in Biomedicine, (CISTIB) – Universitat Pompeu Fabra and CIBER-BBN
  • , Mathieu De CraeneAffiliated withLancaster UniversityCenter for Computational Imaging & Simulation Technologies in Biomedicine, (CISTIB) – Universitat Pompeu Fabra and CIBER-BBN
  • , Gemma PiellaAffiliated withLancaster UniversityCenter for Computational Imaging & Simulation Technologies in Biomedicine, (CISTIB) – Universitat Pompeu Fabra and CIBER-BBN
  • , Alejandro F. FrangiAffiliated withLancaster UniversityCenter for Computational Imaging & Simulation Technologies in Biomedicine, (CISTIB) – Universitat Pompeu Fabra and CIBER-BBN

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Abstract

We propose a technique to represent a pathological pattern as a deviation from normality along a manifold structure. Each subject is represented by a map of local motion abnormalities, obtained from a statistical atlas of motion built from a healthy population. The algorithm learns a manifold from a set of patients with varying degrees of the same pathology. The approach extends recent manifold-learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Individuals are compared to the manifold population through a distance that combines a mapping to the manifold and the path along the manifold to reach its origin. The method 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 38 CRT candidates and 21 healthy volunteers. Experiments highlight the need of nonlinear techniques to learn the studied data, and the relevance of the computed distance for comparing individuals to a specific pathological pattern.