Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct

Abstract : In this article, we present an application of the polyaffine transformations to classify a population of hearts with myocardial infarction. Polyaffine transformations aim at representing motion by the combination of a limited number of affine transformations defined locally on a regional division of the space. We show that these transformations not only serve as a first (non-learnt) dimension reduction, but also allow to interpret each of the parameters and relate them to known clinical parameters. Then, we use standard supervised learning algorithms on these parameters to classify the population between infarcted and non-infarcted subjects. The method is applied on the STACOM statistical shape modeling labeled data consisting of 200 cases, comprising the same number of healthy subjects and patients with infarct. We train classifiers using different standard machine learning algorithms. Finally, we validate our method with 10-fold cross-validation and get more than 95% of correct classification on yet-unseen data. The method is promising and ready to be tested on the remaining 200 test cases of the challenge.