Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images.
Int J Comput Assist Radiol Surg
; 16(10): 1727-1736, 2021 Oct.
Article
in En
| MEDLINE
| ID: mdl-34386900
ABSTRACT
PURPOSE:
Carotid artery atherosclerotic stenosis accounts for 18-25% of ischemic stroke. In the evaluation of carotid atherosclerotic lesions, the automatic, accurate and rapid segmentation of the carotid artery is a priority issue that needs to be addressed urgently. However, the carotid artery area occupies a small target in computed tomography angiography (CTA) images, which affect the segmentation accuracy.METHODS:
We proposed a coarse-to-fine segmentation pipeline with the Multiplanar D-SEA UNet to achieve fully automatic carotid artery segmentation on the entire 3D CTA images, and compared with other four neural networks (3D-UNet, RA-UNet, Isensee-UNet, Multiplanar-UNet) by assessing Dice, Jaccard similarity coefficient, sensitivity, area under the curve and average hausdorff distance.RESULTS:
Our proposed method can achieve a mean Dice score of 91.51% on the 68 neck CTA scans from Beijing Hospital, which remarkably outperforms state-of-the-art 3D image segmentation methods. And the C2F segmentation pipeline can effectively improve segmentation accuracy while avoiding resolution loss.CONCLUSION:
The proposed segmentation method can realize the fully automatic segmentation of the carotid artery and has robust performance with segmentation accuracy, which can be applied into plaque exfoliation and interventional surgery services. In addition, our method is easy to extend to other medical segmentation tasks with appropriate parameter settings.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carotid Arteries
/
Computed Tomography Angiography
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Int J Comput Assist Radiol Surg
Journal subject:
RADIOLOGIA
Year:
2021
Document type:
Article
Affiliation country:
China