@inproceedings{cfe6378dcb5840a98e08763758904595,
title = "Automatic segmentation of left atrial scar from delayed-enhancement magnetic resonance imaging",
abstract = "Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre- and post- radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction making them tedious and prone to high observer variability. In this paper, we propose a novel automatic segmentation algorithm for segmenting LA scar based on a probabilistic tissue intensity model. This is implemented as a Markov random field-based energy formulation and solved using graph-cuts. It was evaluated against an existing semi-automatic approach and expert manual segmentations using 9 patient data sets. Surface representations were used to compare the methods. The segmented LA scar was expressed as a percentage of the total LA surface. Statistical analysis showed that the novel algorithm was not significantly different to the manual method and that it compared more favorably with this than the semi-automatic approach.",
keywords = "Markov random fields, atrial fibrillation, delayed enhancement MRI, graph-cuts, scar segmentation",
author = "Rashed Karim and Aruna Arujuna and Alex Brazier and Jaswinder Gill and Rinaldi, {C. Aldo} and Mark O'Neill and Reza Razavi and Tobias Schaeffter and Daniel Rueckert and Rhode, {Kawal S.}",
year = "2011",
doi = "10.1007/978-3-642-21028-0_8",
language = "English",
isbn = "9783642210273",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "63--70",
booktitle = "Functional Imaging and Modeling of the Heart - 6th International Conference, FIMH 2011, Proceedings",
note = "6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011 ; Conference date: 25-05-2011 Through 27-05-2011",
}