Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation

Yangfan Ni, Geng Chen, Zhan Feng, Heng Cui, Dimitris Metaxas, Shaoting Zhang, Wentao Zhu

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Accurate liver tumor segmentation is a prerequisite for data-driven tumor analysis. Multiphase computed tomography (CT) with extensive liver tumor characteristics is typically used as the most crucial diagnostic basis. However, the large variations in contrast, texture, and tumor structure between CT phases limit the generalization capabilities of the associated segmentation algorithms. Inadequate feature integration across phases might also lead to a performance decrease. To address these issues, we present a domain-adversarial transformer (DA-Tran) network for segmenting liver tumors from multiphase CT images. A DA module is designed to generate domain-adapted feature maps from the non-contrast-enhanced (NC) phase, arterial (ART) phase, portal venous (PV) phase, and delay phase (DP) images. These domain-adapted feature maps are then combined with 3D transformer blocks to capture patch-structured similarity and global context attention. The experimental findings show that DA-Tran produces cutting-edge tumor segmentation outcomes, making it an ideal candidate for this co-segmentation challenge.

源语言英语
主期刊名2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9798350324471
DOI
出版状态已出版 - 2023
活动45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, 澳大利亚
期限: 24 7月 202327 7月 2023

出版系列

姓名Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

会议

会议45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
国家/地区澳大利亚
Sydney
时期24/07/2327/07/23

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