ORIGINAL RESEARCH article

Front. Oncol.

Sec. Cancer Imaging and Image-directed Interventions

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1569083

This article is part of the Research TopicArtificial Intelligence-based Multimodal Imaging and Multi-omics in Medical ResearchView all 5 articles

DynTransNet: Dynamic Transformer Network with Multi-Scale Attention for Liver Cancer Segmentation

Provisionally accepted
  • 1First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China
  • 2Department of Hepatopancreatobiliary Surgery, First Hospital of Ningbo University, Ningbo, China
  • 3Sejong University, Seoul, Seoul, Republic of Korea
  • 4MetaSyntec Co., LTD, George Town, Cayman Islands
  • 5Ningbo Wedge Medical Technology Co., LTD, Ningbo, China
  • 6Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China

The final, formatted version of the article will be published soon.

Hepatocellular carcinoma (HCC), a predominant subtype of liver cancer, remains a major contributor to global cancer mortality. Accurate delineation of liver tumors in CT and MRI scans is critical for treatment planning and clinical decision-making. However, manual segmentation is time-consuming, error-prone, and inconsistent, necessitating reliable automated approaches. This study presents a novel U-shaped segmentation framework inspired by U-Net, designed to enhance accuracy and robustness. The encoder incorporates Dynamic Multi-Head Self-Attention (D-MSA) to capture both global and local spatial dependencies, while the decoder uses skip connections to preserve spatial detail. Additionally, a Feature Mix Module (FM-M) blends multi-scale features, and a Residual Module (RM) refines feature representations and stabilizes training. The proposed framework addresses key challenges such as boundary precision, complex structural relationships, and dataset imbalance. Experimental results demonstrate superior segmentation performance, achieving a mean Dice score of 86.12 on the ATLAS dataset and 93.12 on the LiTS dataset. The proposed method offers a robust, efficient tool for liver tumor segmentation and holds strong potential to streamline diagnostic workflows and improve automated medical image analysis in clinical practice.

Keywords: liver cancer, segmentation, deep learning, Hepatocellular Carcinoma, Liver tumor

Received: 06 Mar 2025; Accepted: 27 May 2025.

Copyright: © 2025 Zheng, Sagar, Chen, Yu, Ying and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yongyi Zeng, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China

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