title: Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans.
abstract: Model-based segmentation approaches have been proven to produce very accurate segmentation results while simultaneously providing an anatomic labeling for the segmented structures. However, variations of the anatomy, as they are often encountered e.g. on the drainage pattern of the pulmonary veins to the left atrium, cannot be represented by a single model. Automatic model selection extends the model-based segmentation approach to handling significant variational anatomies without user interaction. Using models for the three most common anatomical variations of the left atrium, we propose a method that uses an estimation of the local fit of different models to select the best fitting model automatically. Our approach employs the support vector machine for the automatic model selection. The method was evaluated on 42 very accurate segmentations of MRI scans using three different models. The correct model was chosen in 88.1% of the cases. In a second experiment, reflecting average segmentation results, the model corresponding to the clinical classification was automatically found in 78.0% of the cases.
authors.affiliation: Philips Research Laboratories, Hamburg, Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Kutra
authors.forename: Axel
authors.initials: A
authors.lastname: Saalbach
authors.forename: Helko
authors.initials: H
authors.lastname: Lehmann
authors.forename: Alexandra
authors.initials: A
authors.lastname: Groth
authors.forename: Sebastian P M
authors.initials: SP
authors.lastname: Dries
authors.forename: Martin W
authors.initials: MW
authors.lastname: Krueger
authors.forename: Olaf
authors.initials: O
authors.lastname: Dössel
authors.forename: Jürgen
authors.initials: J
authors.lastname: Weese
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006325:Heart Atria / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine