Myocardial Infarct Localization using Neighborhood Approximation Forests

Héloïse Bleton (1) , Jan Margeta (1) , Herve Lombaert (1) , Hervé Delingette (1) , Nicholas Ayache (1)

1 ASCLEPIOS - Analysis and Simulation of Biomedical Images

This paper presents a machine-learning algorithm for the automatic localization of myocardial infarct in the left ventricle. Our method constructs neighbourhood approximation forests, which are trained with previously diagnosed 4D cardiac sequences. We introduce a new set of features that simultaneously exploit information from the shape and motion of the myocardial wall along the cardiac cycle. More precisely, characteristics are extracted from a hyper surface that represents the profile of the myocardial thickness. The method has been tested on a database of 65 cardiac MRI images in order to retrieve the diagnosed infarct area. The results demonstrate the effectiveness of the NAF in predicting the left ventricular infarct location in 7 distinct regions. We evaluated our method by verifying the database ground truth. Following a new examination of the 4D cardiac images, our algorithm may detect misclassified infarct locations in the database.

Mots clés: Machine Learning; Neighbourhood Approximation Forests; myocardial infarction; wall thickness

Domaines: Informatique [cs]

Héloïse Bleton, Jan Margeta, Herve Lombaert, Hervé Delingette, Nicholas Ayache. Myocardial Infarct Localization using Neighborhood Approximation Forests. Statistical Atlases and Computational Modeling of the Heart (STACOM 2015), Oct 2015, Munich, Germany. ⟨hal-01203579⟩