Segmentation of 3D RF echocardiography using a multiframe spatio-temporal predictor.

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.