TY - JOUR
T1 - Fully automated carotid plaque segmentation in combined contrast-enhanced and B-mode ultrasound
AU - Akkus, Zeynettin
AU - Carvalho, Diego D.B.
AU - van den Oord, Stijn C.H.
AU - Schinkel, Arend F.L.
AU - Niessen, Wiro J.
AU - de Jong, Nico
AU - van der Steen, Antonius F.W.
AU - Klein, Stefan
AU - Bosch, Johan G.
N1 - Publisher Copyright:
© 2015 World Federation for Ultrasound in Medicine & Biology.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - B-Mode
KW - Carotid plaques
KW - Contrast-enhanced ultrasound
KW - Lumen-intima segmentation
KW - Media-adventitia segmentation
KW - Plaque segmentation
KW - Vessel detection
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U2 - 10.1016/j.ultrasmedbio.2014.10.004
DO - 10.1016/j.ultrasmedbio.2014.10.004
M3 - Article
C2 - 25542485
AN - SCOPUS:84922739037
SN - 0301-5629
VL - 41
SP - 517
EP - 531
JO - Ultrasound in Medicine and Biology
JF - Ultrasound in Medicine and Biology
IS - 2
ER -