Carotid plaque segmentation in B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) is crucial to the assessment of plaque morphology and composition, which are linked to plaque vulnerability. Segmentation in BMUS is challenging because of noise, artifacts and echo-lucent plaques. CEUS allows better delineation of the lumen but contains artifacts and lacks tissue information. We describe a method that exploits the combined information from simultaneously acquired BMUS and CEUS images. Our method consists of non-rigid motion estimation, vessel detection, lumen-intima segmentation and media-adventitia segmentation. The evaluation was performed in training (n=20 carotids) and test (n=28) data sets by comparison with manually obtained ground truth. The average root-mean-square errors in the training and test data sets were comparable for media-adventitia (411±224 and 393±239μm) and for lumen-intima (362±192 and 388±200μm), and were comparable to inter-observer variability. To the best of our knowledge, this is the first method to perform fully automatic carotid plaque segmentation using combined BMUS and CEUS.

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Ultrasound in Medicine and Biology
Erasmus MC: University Medical Center Rotterdam

Akkus, Z., Carvalho, D., van den Oord, S., Schinkel, A., Niessen, W., de Jong, N., … Bosch, H. (2015). Fully automated carotid plaque segmentation in combined contrast-enhanced and B-mode ultrasound. Ultrasound in Medicine and Biology, 41(2), 517–531. doi:10.1016/j.ultrasmedbio.2014.10.004