Lumen Segmentation and Motion Estimation in B-Mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients With Atherosclerotic Plaque

Diego Carvalho, Z Akkus, Stijn Oord, Arend Schinkel, Ton van der Steen, Wiro Niessen, Hans Bosch, Stefan Klein

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19 Citations (Scopus)


In standard B-mode ultrasound (BMUS), segmentation of the lumen of atherosclerotic carotid arteries and studying the lumen geometry over time are difficult owing to irregular lumen shapes, noise, artifacts, and echolucent plaques. Contrast enhanced ultrasound (CEUS) improves lumen visualization, but lumen segmentation remains challenging owing to varying intensities, CEUS-specific artifacts and lack of tissue visualization. To overcome these challenges, we propose a novel method using simultaneously acquired BMUS&CEUS image sequences. Initially, the method estimates nonrigid motion (NME) from the image sequences, using intensity-based image registration. The motion-compensated image sequence is then averaged to obtain a single "epitome" image with improved signal-to-noise ratio. The lumen is segmented from the epitome image through an intensity joint-histogram classification and a graph-based segmentation. NME was validated by comparing displacements with manual annotations in 11 carotids. The average root mean square error (RMSE) was 112 +/- 73 mu m. Segmentation results were validated against manual delineations in the epitome images of two different datasets, respectively containing 11 (RMSE 191 +/- 43 mu m) and 10 (RMSE 351 +/- 176 mu m) carotids. From the deformation fields, we derived arterial distensibility with values comparable to the literature. The average errors in all experiments were in the inter-observer variability range. To the best of our knowledge, this is the first study exploiting combined BMUS&CEUS images for atherosclerotic carotid lumen segmentation.
Original languageUndefined/Unknown
Pages (from-to)983-993
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number4
Publication statusPublished - 2015

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