Automatic segmentation of left atrial scar from delayed-enhancement magnetic resonance imaging

Authors:
Rashed Karim;Aruna Arujuna;Alex Brazier;Jaswinder Gill;C. Aldo Rinaldi;Mark O'Neill;Reza Razavi;Tobias Schaeffter;Daniel Rueckert;Kawal S. Rhode
Affiliations:
Division of Imaging Sciences and Biomedical Engineering, King's College London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK and Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Trust, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK and Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Trust, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK and Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Trust, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK and Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Trust, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK and Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Trust, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK;Department of Computing, Imperial College London, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
Venue:
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Year:
2011

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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.