DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation
IntroductionHepatocellular carcinoma (HCC), a predominant subtype of liver cancer, remains Q7 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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1569083/full |
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| author | Siming Zheng Siming Zheng A. S. M. Sharifuzzaman Sagar Yu Chen Zehao Yu Shi Ying Yongyi Zeng Yongyi Zeng |
| author_facet | Siming Zheng Siming Zheng A. S. M. Sharifuzzaman Sagar Yu Chen Zehao Yu Shi Ying Yongyi Zeng Yongyi Zeng |
| author_sort | Siming Zheng |
| collection | DOAJ |
| description | IntroductionHepatocellular carcinoma (HCC), a predominant subtype of liver cancer, remains Q7 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, errorprone, and inconsistent, necessitating reliable automated approaches.MethodsThis 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 multiscale 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.ResultsExperimental results demonstrate superior segmentation performance, achieving a mean Dice score of 86.12 on the ATLAS dataset and 93.12 on the LiTS dataset.DiscussionThe 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. |
| format | Article |
| id | doaj-art-7872f4f1aef14671a7a10f3a7d8ce4db |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-7872f4f1aef14671a7a10f3a7d8ce4db2025-08-20T03:30:44ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-06-011510.3389/fonc.2025.15690831569083DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentationSiming Zheng0Siming Zheng1A. S. M. Sharifuzzaman Sagar2Yu Chen3Zehao Yu4Shi Ying5Yongyi Zeng6Yongyi Zeng7The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Hepatopancreatobiliary Surgery, First Hospital of Ningbo University, Ningbo, ChinaDepartment of Artificial and Robotics, Sejong University, Seoul, Republic of KoreaAI Lab, MetaSyntec Co., LTD, George Town, Cayman IslandsDepartment of Hepatopancreatobiliary Surgery, First Hospital of Ningbo University, Ningbo, ChinaDepartment of Engineering Technology, Ningbo Wedge Medical Technology Co., LTD, Ningbo, ChinaThe First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, ChinaIntroductionHepatocellular carcinoma (HCC), a predominant subtype of liver cancer, remains Q7 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, errorprone, and inconsistent, necessitating reliable automated approaches.MethodsThis 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 multiscale 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.ResultsExperimental results demonstrate superior segmentation performance, achieving a mean Dice score of 86.12 on the ATLAS dataset and 93.12 on the LiTS dataset.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1569083/fullliver cancersegmentationdeep learninghepatocellular carcinomaliver tumor |
| spellingShingle | Siming Zheng Siming Zheng A. S. M. Sharifuzzaman Sagar Yu Chen Zehao Yu Shi Ying Yongyi Zeng Yongyi Zeng DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation Frontiers in Oncology liver cancer segmentation deep learning hepatocellular carcinoma liver tumor |
| title | DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation |
| title_full | DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation |
| title_fullStr | DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation |
| title_full_unstemmed | DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation |
| title_short | DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation |
| title_sort | dyntransnet dynamic transformer network with multi scale attention for liver cancer segmentation |
| topic | liver cancer segmentation deep learning hepatocellular carcinoma liver tumor |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1569083/full |
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