3D Cardiac Segmentation Using Temporal Correlation of Radio Frequency Ultrasound Data
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
LV segmentation through the analysis of radio frequency ultrasonic images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
IEEE Transactions on Image Processing
Segmentation of 3D RF echocardiography using a multiframe spatio-temporal predictor
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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We present an approach for segmenting the left ventricular endocardial boundaries from radio-frequency (RF) ultrasound. The method employs a computationally efficient two-frame linear predictor which exploits the spatio-temporal coherence of the data. By performing segmentation using the RF data we are able to overcome problems due to image inhomogeneities that are often amplified in B-mode segmentation, as well as provide geometric constraints for RF phase-based speckle tracking. We illustrate the advantages of our approach by comparing it to manual tracings of B-mode data and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 28 3D sequences acquired from 6 canine studies, imaged both at baseline and 1 hour post infarction.