title: Segmentation of 3D RF echocardiography using a multiframe spatio-temporal predictor.
abstract: We present an approach for segmenting left ventricular endocardial boundaries from RF ultrasound. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model. The conditional model relates neighboring frames in the image sequence by means of a computationally efficient linear predictor that exploits spatio-temporal coherence in the data. Segmentation using the RF data overcomes problems 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 6 canine studies.
authors.affiliation: Department of Electrical Engineering, Yale University, New Haven, CT, USA. paul.pearlman@yale.edu
authors.forename: Paul C
authors.initials: PC
authors.lastname: Pearlman
authors.forename: Hemant D
authors.initials: HD
authors.lastname: Tagare
authors.forename: Ben A
authors.initials: BA
authors.lastname: Lin
authors.forename: Albert J
authors.initials: AJ
authors.lastname: Sinusas
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D000818:Animals; D004285:Dogs; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity