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Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images by Yanan Wu; Shouliang Qi; Meihuan Wang; Shuiqing Zhao; Haowen Pang; Jiaxuan Xu; Long Bai; Hongliang Ren is a Medicine article available to read on EtoBox.

What is Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images about?

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 separ

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Author

Yanan Wu; Shouliang Qi; Meihuan Wang; Shuiqing Zhao; Haowen Pang; Jiaxuan Xu; Long Bai; Hongliang Ren

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Medicine (Health Sciences)