title: Automatic cardiac MRI segmentation using a biventricular deformable medial model.
abstract: We present a novel approach for automatic segmentation of the myocardium in short-axis MRI using deformable medial models with an explicit representation of thickness. Segmentation is constrained by a Markov prior on myocardial thickness. Best practices from Active Shape Modeling (global PCA shape prior, statistical appearance model, local search) are adapted to the medial model. Segmentation performance is evaluated by comparing to manual segmentation in a heterogeneous adult MRI dataset. Average boundary displacement error is under 1.4 mm for left and right ventricles, comparing favorably with published work.
authors.affiliation: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Sun
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.forename: Federico M
authors.initials: FM
authors.lastname: Sukno
authors.forename: Catalina
authors.initials: C
authors.lastname: Tobon-Gomez
authors.forename: Paul A
authors.initials: PA
authors.lastname: Yushkevich
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis*