title: 3D cardiac segmentation using temporal correlation of radio frequency ultrasound data.
abstract: Semi-automatic segmentation of the myocardium in 3D echographic images may substantially support clinical diagnosis of heart disease. Particularly in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. Segmentation of echocardiographic images is challenging because of the poor echogenicity contrast between blood and the myocardium in some regions and the inherent speckle noise from randomly backscattered echoes. Phase information present in the radio frequency (rf) ultrasound data might yield useful, additional features in these regions. A semi-3D technique was used to determine maximum temporal cross-correlation values locally from the rf data. To segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf data. The method was tested on full volume images (Philips, iE33) of four healthy children and evaluated by comparison with contours obtained from manual segmentation.
authors.affiliation: Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre. m.m.nillesen@cukz.umcn.nl
authors.forename: Maartje M
authors.initials: MM
authors.lastname: Nillesen
authors.forename: Richard G P
authors.initials: RG
authors.lastname: Lopata
authors.forename: Henkjan J
authors.initials: HJ
authors.lastname: Huisman
authors.forename: Johan M
authors.initials: JM
authors.lastname: Thijssen
authors.forename: Livia
authors.initials: L
authors.lastname: Kapusta
authors.forename: Chris L
authors.initials: CL
authors.lastname: de Korte
mesh_terms: D000465:Algorithms; D002648:Child; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011846:Radio Waves; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013382:Subtraction Technique; D014463:Ultrasonography / Q000379:methods*