CGP-Net: Cross-modal guided prior network for precise gastric cancer segmentation_Journal of Biomedical Engineering

Authors：

GE Chaoyang ^1,2 , GAO Yifan ^1,2 , LIU Cheng ^1,2 ,  GAO Xin ^2,3

1. Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, P. R. China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China;
3. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan 250101, P. R. China;

Corresponding?author：

GAO Xin, Email: xingaosam@163.com

Keywords：

Gastric cancer segmentation; Text guidance; State-space model; Cross-modal fusion

DOI：

10.7507/1001-5515.202507011

Video：

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Abstract Full text Figures/Tables Video References Cited by

Precise segmentation of gastric cancer computed tomography (CT) images is a critical step for clinical precision diagnosis and treatment. However, it currently faces two major challenges: the low contrast between tumors and surrounding normal tissues makes boundary delineation difficult, and the high variability in tumor shape, size, and location leads to inaccurate localization. To address these issues, a cross-modal prior knowledge-guided gastric cancer CT image automatic segmentation method (CGP-Net) was proposed. In this method, visual priors were extracted from diagnostic reports using a large language model (LLM), and lesion localization was assisted by a semantic anchoring and parsing module. A mixed context-aware Mamba module was constructed to synergistically optimize feature modeling for adapting to tumor morphological variations. Furthermore, a boundary-aware gated convolution module was designed to improve the delineation accuracy of fuzzy boundaries. Experiments on a large-scale dataset of 349 gastric cancer patients demonstrated that the Dice coefficient and 95th percentile of Hausdorff distance (HD95) of the proposed method reached 78.10% and 16.44 mm, respectively. It outperformed state-of-the-art methods such as U-Mamba and nnUNet in terms of segmentation accuracy and boundary prediction. This method effectively integrates textual priors to significantly enhance segmentation accuracy, offering significant value for clinical applications.

Citation： GE Chaoyang, GAO Yifan, LIU Cheng, GAO Xin. CGP-Net: Cross-modal guided prior network for precise gastric cancer segmentation. Journal of Biomedical Engineering, 2026, 43(1): 146-153. doi: 10.7507/1001-5515.202507011 Copy

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17.	Xing Z, Ye T, Yang Y, et al. SegMamba: Long-range sequential modeling Mamba for 3D medical image segmentation// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2024. Cham: Springer, 2024: 578-588.
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21.	Ma J, Li F, Wang B. U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv, 2024: 2401.04722.
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26.	Kumar G M K, Chadha A, Mendola J, et al. MedVisionLlama: Leveraging pre-trained large language model layers to enhance medical image segmentation// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops. New York: IEEE, 2025: 1114-1124.
27.	Wu J, Fu R, Fang H, et al. Medical SAM adapter: Adapting segment anything model for medical image segmentation. Med Image Anal, 2025, 102: 103547.
28.	Li Y, Lai Z, Bao W, et al. Visual large language models for generalized and specialized applications. arXiv preprint arXiv, 2025: 2501.02765.
29.	Chen W, Liu J, Liu T, et al. Bi-VLGM: Bi-level class-severity-aware vision-language graph matching for text guided medical image segmentation. Int J Comput Vis, 2025, 133(3): 1375-1391.
30.	Rao Y, Zhao W, Chen G, et al. Denseclip: Language-guided dense prediction with context-aware prompting// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2022: 18082-18091.
31.	Yuan R, Chen M, Xu J, et al. Text-promptable propagation for referring medical image sequence segmentation. arXiv preprint arXiv, 2025: 2502.11093.
32.	He J, Wang G, Zhang Q, et al. ReMamber: Referring image segmentation with mamba twister. arXiv preprint arXiv, 2024: 2404.11651.
33.	Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
34.	Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer, 2022: 272-284.
35.	Zhang Y, Liu H, Hu Q. TransFuse: Fusing transformers and CNNs for medical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer, 2021: 14-24.
36.	Wang Z, Zheng J, Zhang Y, et al. Mamba-UNet: A mamba-based u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2402.08699.
37.	Ruan J, Li J, Xiang S. VM-UNet: Vision mamba u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2402.02491.
38.	Liu J, Yang H, Zhou H, et al. Swin-UMamba: Adapting mamba-based vision foundation models for medical image segmentation. arXiv preprint arXiv, 2024: 2402.03302.
39.	Wu R, Liu Y, Liang L, et al. H-vmunet: High-order vision mamba u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2403.13642.
40.	Li Y, Li X, Liu Y, et al. MLLA-UNet: Mamba-like linear attention in an efficient u-shape model for medical image segmentation. arXiv preprint arXiv, 2024: 2405.02875.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Morgan E, Arnold M, Camargo M C, et al. The current and future incidence and mortality of gastric cancer in 185 countries, 2020-40: A population-based modelling study. EClinicalMedicine, 2022, 47: 101404.
3. Yang W J, Zhao H P, Yu Y, et al. Updates on global epidemiology, risk and prognostic factors of gastric cancer. World J Gastroenterol, 2023, 29(16): 2452-2468.
4. Lordick F, Carneiro F, Cascinu S, et al. Gastric cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol, 2022, 33(10): 1005-1020.
5. Hallinan J T P D, Venkatesh S K. Gastric carcinoma: Imaging diagnosis, staging and assessment of treatment response. Cancer Imaging, 2013, 13(2): 212-227.
6. Zhang Y, Yuan N, Zhang Z, et al. Unsupervised domain selective graph convolutional network for preoperative prediction of lymph node metastasis in gastric cancer. Med Image Anal, 2022, 79: 102467.
7. 胡倫瑜, 夏威, 李瓊, 等. 基于自監督預訓練和多任務學習的肺腺癌無復發生存期預測. 生物醫學工程學雜志, 2024, 41(2): 205-212.
8. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Cham: Springer, 2015: 234-241.
9. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv, 2018: 1804.03999.
10. Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: A nested U-Net architecture for medical image segmentation// Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer, 2018: 3-11.
11. Shang H, Feng T, Han D, et al. Deep learning and radiomics for gastric cancer serosal invasion: Automated segmentation and multi-machine learning from two centers. J Cancer Res Clin Oncol, 2025, 151(2): 60.
12. Wang J, Zhang B, Wang Y, et al. CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer. Comput Med Imaging Graph, 2024, 112: 102339.
13. Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv, 2021: 2102.04306.
14. Hatamizadeh A, Tang Y, Nath V, et al. UNETR: Transformers for 3D medical image segmentation// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: IEEE, 2022: 1748-1758.
15. Cao H, Wang Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation// European Conference on Computer Vision (ECCV). Cham: Springer, 2022: 205-218.
16. Liu Y, Tian Y, Zhao Y, et al. VMamba: Visual state space model. Adv Neural Inf Process Syst, 2024, 37: 103031-103063.
17. Xing Z, Ye T, Yang Y, et al. SegMamba: Long-range sequential modeling Mamba for 3D medical image segmentation// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2024. Cham: Springer, 2024: 578-588.
18. Wang J, Zheng J, Ma L, et al. LKM-UNet: Large kernel vision Mamba U-Net for medical image segmentation// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2024. Cham: Springer, 2024: 360-370.
19. Bansal S, Madisetty S, Rehman M Z U, et al. A comprehensive survey of Mamba architectures for medical image analysis: Classification, segmentation, restoration and beyond. arXiv preprint arXiv, 2024: 2410.02362.
20. Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv, 2023: 2312.00752.
21. Ma J, Li F, Wang B. U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv, 2024: 2401.04722.
22. Ling T, Zuo Z, Huang M, et al. Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy. BMC Cancer, 2025, 25(1): 834.
23. Zheng G, Wang H, Chai X, et al. Interpretable deep learning for multicenter gastric cancer T staging from CT images. npj Digit Med, 2026, 9: 2.
24. Li L, Wang C, Geng Y, et al. Segment anything model for gastric cancer. Cancer Med, 2025, 14(18): e71246.
25. Bhardwaj P, Kumar S, Kumar Y. Deep learning techniques in gastric cancer prediction and diagnosis// 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON). New York: IEEE, 2022: 843-850.
26. Kumar G M K, Chadha A, Mendola J, et al. MedVisionLlama: Leveraging pre-trained large language model layers to enhance medical image segmentation// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops. New York: IEEE, 2025: 1114-1124.
27. Wu J, Fu R, Fang H, et al. Medical SAM adapter: Adapting segment anything model for medical image segmentation. Med Image Anal, 2025, 102: 103547.
28. Li Y, Lai Z, Bao W, et al. Visual large language models for generalized and specialized applications. arXiv preprint arXiv, 2025: 2501.02765.
29. Chen W, Liu J, Liu T, et al. Bi-VLGM: Bi-level class-severity-aware vision-language graph matching for text guided medical image segmentation. Int J Comput Vis, 2025, 133(3): 1375-1391.
30. Rao Y, Zhao W, Chen G, et al. Denseclip: Language-guided dense prediction with context-aware prompting// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2022: 18082-18091.
31. Yuan R, Chen M, Xu J, et al. Text-promptable propagation for referring medical image sequence segmentation. arXiv preprint arXiv, 2025: 2502.11093.
32. He J, Wang G, Zhang Q, et al. ReMamber: Referring image segmentation with mamba twister. arXiv preprint arXiv, 2024: 2404.11651.
33. Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
34. Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer, 2022: 272-284.
35. Zhang Y, Liu H, Hu Q. TransFuse: Fusing transformers and CNNs for medical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer, 2021: 14-24.
36. Wang Z, Zheng J, Zhang Y, et al. Mamba-UNet: A mamba-based u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2402.08699.
37. Ruan J, Li J, Xiang S. VM-UNet: Vision mamba u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2402.02491.
38. Liu J, Yang H, Zhou H, et al. Swin-UMamba: Adapting mamba-based vision foundation models for medical image segmentation. arXiv preprint arXiv, 2024: 2402.03302.
39. Wu R, Liu Y, Liang L, et al. H-vmunet: High-order vision mamba u-net for medical image segmentation. arXiv preprint arXiv, 2024: 2403.13642.
40. Li Y, Li X, Liu Y, et al. MLLA-UNet: Mamba-like linear attention in an efficient u-shape model for medical image segmentation. arXiv preprint arXiv, 2024: 2405.02875.

Journal of Biomedical Engineering

CGP-Net: Cross-modal guided prior network for precise gastric cancer segmentation

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