TY - GEN
T1 - Segmentation of 3D ultrasound carotid vessel wall using U-Net and segmentation average network
AU - Jiang, Mingjie
AU - Spence, J. David
AU - Chiu, Bernard
PY - 2020/7
Y1 - 2020/7
N2 - Segmentation of carotid vessel wall is required
in vessel wall volume (VWV) and local vessel-wall-plus-plaque
thickness (VWT) quantification of the carotid artery. Manual
segmentation of the vessel wall is time-consuming and prone
to interobserver variability. In this paper, we proposed a
convolutional neural network (CNN) to segment the common
carotid artery (CCA) from 3D carotid ultrasound images. The
proposed CNN involves three U-Nets that segmented the 3D
ultrasound (3DUS) images in the axial, lateral and frontal
orientations. The segmentation maps generated by three U-Nets
were consolidated by a novel segmentation average network
(SAN) we proposed in this paper. The experimental results show
that the proposed CNN improved the segmentation accuracies.
Compared to only using U-Net alone, the proposed CNN
improved the Dice similarity coefficient (DSC) for vessel wall
segmentation from 64.8% to 67.5%, the sensitivity from 63.8%
to 70.5%, and the area under receiver operator characteristic
curve (AUC) from 0.89 to 0.94.
AB - Segmentation of carotid vessel wall is required
in vessel wall volume (VWV) and local vessel-wall-plus-plaque
thickness (VWT) quantification of the carotid artery. Manual
segmentation of the vessel wall is time-consuming and prone
to interobserver variability. In this paper, we proposed a
convolutional neural network (CNN) to segment the common
carotid artery (CCA) from 3D carotid ultrasound images. The
proposed CNN involves three U-Nets that segmented the 3D
ultrasound (3DUS) images in the axial, lateral and frontal
orientations. The segmentation maps generated by three U-Nets
were consolidated by a novel segmentation average network
(SAN) we proposed in this paper. The experimental results show
that the proposed CNN improved the segmentation accuracies.
Compared to only using U-Net alone, the proposed CNN
improved the Dice similarity coefficient (DSC) for vessel wall
segmentation from 64.8% to 67.5%, the sensitivity from 63.8%
to 70.5%, and the area under receiver operator characteristic
curve (AUC) from 0.89 to 0.94.
UR - http://www.scopus.com/inward/record.url?scp=85091019636&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85091019636&origin=recordpage
U2 - 10.1109/EMBC44109.2020.9175975
DO - 10.1109/EMBC44109.2020.9175975
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2043
EP - 2046
BT - 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
PB - IEEE
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)
Y2 - 20 July 2020 through 24 July 2020
ER -