Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium‐enhanced MRI: Towards objective atrial scar assessment

To realize objective atrial scar assessment, this study aimed to develop a fully automatic method to segment the left atrium (LA) and pulmonary veins (PV) from late gadolinium‐enhanced (LGE) magnetic resonance imaging (MRI). The extent and distribution of atrial scar, visualized by LGE‐MRI, provides important information for clinical treatment of atrial fibrillation (AF) patients.

[1]  V. Fuster,et al.  Management of patients with atrial fibrillation. A Statement for Healthcare Professionals. From the Subcommittee on Electrocardiography and Electrophysiology, American Heart Association. , 1996, Circulation.

[2]  O. Simonetti,et al.  The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. , 2000, The New England journal of medicine.

[3]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[4]  Torsten Rohlfing,et al.  Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[7]  H. Halperin,et al.  Integrated Electroanatomic Mapping With Three-Dimensional Computed Tomographic Images for Real-Time Guided Ablations , 2006, Circulation.

[8]  T. Dickfeld,et al.  Image integration in electroanatomic mapping , 2007, Herzschrittmachertherapie & Elektrophysiologie.

[9]  L.-K. Shark,et al.  Medical Image Segmentation Using New Hybrid Level-Set Method , 2008, 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics.

[10]  Bart M. ter Haar Romeny,et al.  Cardiac left atrium CT image segmentation for ablation guidance , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Sébastien Ourselin,et al.  A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI , 2010, IEEE Transactions on Medical Imaging.

[12]  Max A. Viergever,et al.  Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE) , 2010, IEEE Transactions on Medical Imaging.

[13]  Olivier Ecabert,et al.  Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation , 2010, Medical Image Anal..

[14]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[15]  Nassir F Marrouche,et al.  Tailored Management of Atrial Fibrillation Using a LGE‐MRI Based Model: From the Clinic to the Electrophysiology Laboratory , 2011, Journal of cardiovascular electrophysiology.

[16]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[17]  Alistair A. Young,et al.  Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges , 2013, Lecture Notes in Computer Science.

[18]  Jürgen Weese,et al.  Left Atrial Segmentation Challenge: A Unified Benchmarking Framework , 2013, STACOM.

[19]  Daniel J. Perry,et al.  Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge , 2013, Journal of Cardiovascular Magnetic Resonance.

[20]  Nazem Akoum,et al.  Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. , 2014, JAMA.

[21]  Vadim Zipunnikov,et al.  Magnetic resonance image intensity ratio, a normalized measure to enable interpatient comparability of left atrial fibrosis. , 2014, Heart rhythm.

[22]  Jürgen Weese,et al.  Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets , 2015, IEEE Transactions on Medical Imaging.

[23]  Marlien Herselman,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2015 .