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Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images.
- Source :
-
Medical & Biological Engineering & Computing . Oct2023, Vol. 61 Issue 10, p2649-2663. 15p. 4 Color Photographs, 1 Black and White Photograph, 1 Diagram, 5 Charts, 3 Graphs. - Publication Year :
- 2023
-
Abstract
- Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01400118
- Volume :
- 61
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Medical & Biological Engineering & Computing
- Publication Type :
- Academic Journal
- Accession number :
- 172361464
- Full Text :
- https://doi.org/10.1007/s11517-023-02872-5