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Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets

Catalina Tobon-Gomez, Arjan J. Geers, Jochen Peters, Jurgen Weese, Karen Pinto, Rashed Karim, Mohammed Ammar, Abdelaziz Daoudi, Jan Margeta, Zulma Sandoval, Birgit Stender, Yefeng Zheng (+12 others)
2015 IEEE Transactions on Medical Imaging  

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Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript
more » ... also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standard-
doi:10.1109/tmi.2015.2398818 pmid:25667349 fatcat:x6mkjar7cfdqfic6md5uzt2q6u
Citation

Catalina Tobon-Gomez, Arjan J. Geers, Jochen Peters, Jurgen Weese, Karen Pinto, Rashed Karim, Mohammed Ammar, Abdelaziz Daoudi, Jan Margeta, Zulma Sandoval, Birgit Stender, Yefeng Zheng, Maria A. Zuluaga, Julian Betancur, Nicholas Ayache, Mohammed Amine Chikh, Jean-Louis Dillenseger, B. Michael Kelm, Said Mahmoudi, Sebastien Ourselin, Alexander Schlaefer, Tobias Schaeffter, Reza Razavi, Kawal S. Rhode. "Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets." IEEE Transactions on Medical Imaging 34.7 (2015) 1460-1473