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

Jan Margeta 1 Ezequiel Geremia 1 Antonio Criminisi 2 Nicholas Ayache 1 
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
2 Microsoft - Microsoft Research [Cambridge]
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 classi cation with decision forests and propose a method for context aware MR intensity standardization and image alignment. The second layer is then used for the nal image segmentation. We present our rst results on the STACOM LV Segmentation Challenge 2011 validation datasets.
keyword : segmentation cardiac magnetic resonance left ventricle
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Conference papers
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https://hal.inria.fr/hal-00646674
Contributor : Jan Margeta Connect in order to contact the contributor
Submitted on : Wednesday, November 30, 2011 - 3:11:09 PM
Last modification on : Saturday, June 25, 2022 - 11:06:57 PM

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Jan Margeta, Ezequiel Geremia, Antonio Criminisi, Nicholas Ayache. Layered spatio-temporal forests for left ventricle segmentation from 4D cardiac MRI data. STACOM Workshop at MICCAI 2011, Oscar Camara and Ender Konukoglu and Mihaela Pop and Kawal Rhode and Maxime Sermesant and Alistair Young, Sep 2011, Toronto, Canada. pp.109-119, ⟨10.1007/978-3-642-28326-0_11⟩. ⟨hal-00646674⟩

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