Layered spatio-temporal forests for left ventricle segmentation from 4d cardiac MRI data

Authors:
J$#225/n Margeta;Ezequiel Geremia;Antonio Criminisi;Nicholas Ayache
Affiliations:
Asclepios Research Project, INRIA, Sophia-Antipolis, France;Asclepios Research Project, INRIA, Sophia-Antipolis, France;Machine Learning and Perception Group, Microsoft Research, Cambridge, UK;Asclepios Research Project, INRIA, Sophia-Antipolis, France
Venue:
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2011

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Abstract

In this paper we present a new method for fully automatic left ventricle segmentation from 4D cardiac MR datasets. To deal with the diverse dataset, we propose a machine learning approach using two layers of spatio-temporal decision forests with almost no assumptions on the data nor explicitly specifying the segmentation rules. We introduce 4D spatio-temporal features to classification with decision forests and propose a method for context aware MR intensity standardization and image alignment. The second layer is then used for the final image segmentation. We present our first results on the STACOM LV Segmentation Challenge 2011 validation datasets.