Robust atlas-based segmentation of highly variable anatomy: left atrium segmentation

Authors:
Michal Depa;Mert R. Sabuncu;Godtfred Holmvang;Reza Nezafat;Ehud J. Schmidt;Polina Golland
Affiliations:
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA;Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA;Cardiac MRI Unit, Massachusetts General Hospital, Boston, MA;Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center, Boston, MA;Department of Radiology, Brigham & Women's Hospital, Boston, MA;Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA
Venue:
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Year:
2010

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Abstract

Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.