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

Journal
IEEE transactions on medical imaging  
Volume
34
Issue
7
Start Page
1460
End Page
1473
Article Number
7029623
Citation
IEEE Transactions on Medical Imaging 7 (34): 7029623 1460-1473 (2015-07-01)
Publisher DOI
10.1109/TMI.2015.2398818
Scopus ID
2-s2.0-84936755135
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 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 standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.
Subjects
benchmark testing
cardiovascular disease
computed tomography
Image segmentation
left atrium
magnetic resonance imaging