Abstract
Precise segmentation of carotid artery (CA) structure is an important prerequisite
for the medical assessment and detection of carotid plaques. For automatic segmentation
of the media–adventitia boundary (MAB) and lumen–intima boundary (LIB) in 3-D ultrasound
images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder
and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture
rich global context information in the 3-D image. Experiments were performed on a
3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT
performs better than other convolutional neural network (CNN)-based and CNN–transformer
hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1%
for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ±
6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become
an effective method for automatic segmentation of 3-D ultrasound images of CA.
Keywords
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Article info
Publication history
Published online: November 29, 2022
Accepted:
November 6,
2022
Received in revised form:
October 22,
2022
Received:
March 9,
2022
Identification
Copyright
© 2022 Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology.
