Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas

Authors:
R. Karim;C. Juli;L. Malcolme-Lawes;D. Wyn-Davies;P. Kanagaratnam;N. Peters;D. Rueckert
Affiliations:
Department of Computing, Imperial College London, South Kensington, London, National Heart and Lung Institute, Imperial College London, South Kensington, London;Imaging Department, St. Mary's Hospital, London;National Heart and Lung Institute, Imperial College London, South Kensington, London;National Heart and Lung Institute, Imperial College London, South Kensington, London;National Heart and Lung Institute, Imperial College London, South Kensington, London;National Heart and Lung Institute, Imperial College London, South Kensington, London;Department of Computing, Imperial College London, South Kensington, London
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

Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting the atrial geometry. Nevertheless, the variable anatomy of the atrium poses significant challenge for segmentation. To overcome the inherent difficulties with this segmentation, we propose a technique that utilizes the Voronoi subdivision framework for the segmentation. In addition, the segmentation is based on the minimization of a Markov Random Field based energy functional defined within the Voronoi framework. The method also incorporates anatomical priors in the form of a probabilistic atlas. We show how the model is efficient in segmenting atrium images by comparing results from manual segmentations.