Paper Info
Title
Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images
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.
Year
DOI
Venue
2021
10.1007/s11548-021-02471-5
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Keywords
DocType
Volume
Carotid segmentation, Coarse-to-fine, UNet, 3D CTA
Journal
16
Issue
ISSN
Citations 
10
1861-6410
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Junjie Wang100.34
Yuanyuan Yu200.34
Rongyao Yan300.34
Jie Liu4544.99
Hao Wu500.34
Daoying Geng600.34
Zekuan Yu702.70