title: Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation.
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.
authors.affiliation: Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
authors.forename: Michal
authors.initials: M
authors.lastname: Depa
authors.affiliation: Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Mert R
authors.initials: MR
authors.lastname: Sabuncu
authors.affiliation: Cardiac MRI Unit, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Godtfred
authors.initials: G
authors.lastname: Holmvang
authors.affiliation: Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center, Boston, MA, USA.
authors.forename: Reza
authors.initials: R
authors.lastname: Nezafat
authors.affiliation: Department of Radiology, Brigham & Women's Hospital, Boston, MA, USA.
authors.forename: Ehud J
authors.initials: EJ
authors.lastname: Schmidt
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
keywords: Atlas-based segmentation; cardiac segmentation; label fusion; left atrium segmentation; non-rigid registration