title: Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor.
abstract: This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.
authors.affiliation: Department of Electrical Engineering, Yale University, New Haven, CT 06520-8042, USA. paulp@isi.uu.nl
authors.forename: P C
authors.initials: PC
authors.lastname: Pearlman
authors.forename: H D
authors.initials: HD
authors.lastname: Tagare
authors.forename: B A
authors.initials: BA
authors.lastname: Lin
authors.forename: A J
authors.initials: AJ
authors.lastname: Sinusas
authors.forename: J S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004285:Dogs; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
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