Lung nodule segmentation method based on multiscale feature interaction and coordinate information_Journal of Biomedical Engineering

Authors：

XU Qinglong ^1,4 , ZHU Haixing ² , WANG Yuan ² ,  LIU Weipeng ^2,3

1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
2. School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, P. R. China;
3. Innovation and Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050299, P. R. China;
4. Shinva Medical Instrument Co., Ltd., Zibo, Shandong 255086, P. R. China;

Corresponding?author：

LIU Weipeng, Email: liuweipeng@hebut.edu.cn

Keywords：

Lung nodule segmentation; Transformer; Mamba; Multiscale feature; Attention mechanism

DOI：

10.7507/1001-5515.202505072

Video：

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Abstract Full text Figures/Tables Video References Cited by

For pulmonary nodules in computed tomography (CT) images, which exhibit complex morphology and blurred boundaries, existing segmentation methods still fall short in modelling cross-level dependencies of multi-scale features, thereby limiting their performance in pulmonary nodule segmentation tasks. To address these challenges, this paper proposes a semantic segmentation method for pulmonary nodules based on multiscale feature interaction and cross-level coordinate attention (MFI-CLCA). This U-shaped network incorporated three architectures: a convolutional neural network (CNN), a Transformer, and Mamba. During the encoding phase, combining CNN and Mamba learning paradigms capured both global and local information in the input data. The convolutional component extracted complex boundary features of the target by combining multi-scale convolutional operations with adaptive fusion operations. Global and local multi-head attention mechanisms were introduced in the bottleneck layer and decoding phase respectively to model these hierarchical feature dependencies. The skip-connection section incorporated a multi-level coordinate attention module to adaptively focus on the information being passed through. Experimental results on the Lung Image Database Consortium (LIDC) dataset demonstrated that this approach achieved Dice scores of 90.52% and sensitivity of 91.93%, which outperforms existing state-of-the-art methods and validates its effectiveness for lung nodule segmentation tasks.

Citation： XU Qinglong, ZHU Haixing, WANG Yuan, LIU Weipeng. Lung nodule segmentation method based on multiscale feature interaction and coordinate information. Journal of Biomedical Engineering, 2026, 43(2): 353-361. doi: 10.7507/1001-5515.202505072 Copy

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6.	Agnes S A, Solomon A A, Karthick K. Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: improving early detection and diagnosis of lung cancer. Biomed Signal Process Control, 2024, 87: 105509.
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12.	Huang Z Y, Zhao Z Y, Yu Z, et al. SCFMUNet: a fusion architecture based on multi-scale state space model and channel attention for medical image segmentation. Neural Netw, 2025, 192: 107919.
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15.	Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv, 2023: 2312.00752.
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17.	Hatamizadeh A, Kautz J. MambaVision: a hybrid Mamba-transformer vision backbone// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2024). New York: IEEE, 2024: 25261-25270.
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25.	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.
26.	Zhou H Y, Guo J S, Zhang Y H, et al. nnFormer: volumetric medical image segmentation via a 3D transformer. IEEE Trans Image Process, 2023, 32: 4036-4045.
27.	Yang L X, Zhang R Y, Li L D, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks// 13th International Conference on Machine Learning (ICML). New York: ACM, 2021: 11863-11874.
28.	Wang Q L, Wu B G, Zhu P F, et al. ECA-net: efficient channel attention for deep convolutional neural networks// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2020). New York: IEEE, 2020: 11534-11542.
29.	Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2021). New York: IEEE, 2021: 13713-13722.
30.	Armato S G, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
31.	Müller D, Soto-Rey I, Kramer F. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res Notes, 2022, 15(1): 210.
32.	Oktay O, Schlemper J, Le Folgoc L, et al. Attention U-net: learning where to look for the pancreas. arXiv, 2018: 1804.03999.
33.	Cao H, Wang Y Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation// European Conference on Computer Vision (ECCV2022). Berlin: Springer, 2022: 205-218.
34.	Dumitru R G, Peteleaza D, Craciun C. Using Duck-Net for polyp image segmentation. Sci Rep, 2023, 13(1): 9803.
35.	Wang Z Y, Zheng J Q, Zhang Y C, et al. Mamba-UNet: Unet-like pure visual Mamba for medical image segmentation. arXiv, 2024: 2402.05079.
36.	Hu J, Shen L, Sun G. Squeeze-and-excitation networks// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2018). New York: IEEE, 2018: 7132-7141.
37.	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module// European Conference on Computer Vision (ECCV2022). Berlin: Springer, 2018: 3-19.
38.	Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2019). New York: IEEE, 2019: 3146-3154.

1. Zhang J J, Xia Y, Cui H F, et al. Pulmonary nodule detection in medical images: a survey. Biomed Signal Process Control, 2018, 43: 138-147.
2. Wen M M, Zheng Q, Ji X H, et al. Precise diagnosis and prognosis assessment of malignant lung nodules: a narrative review. J Thorac Dis, 2024, 16(11): 7999-8013.
3. De Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging, 2023, 104(1): 11-17.
4. Teng Z X, Li L, Xin Z Q, et al. A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI. Quant Imaging Med Surg, 2024, 14(12): 9620-9652.
5. Li X T, Jiang A L, Wang S H, et al. CTBP-net: lung nodule segmentation model based on the cross-transformer and bidirectional pyramid. Biomed Signal Process Control, 2023, 82: 104528.
6. Agnes S A, Solomon A A, Karthick K. Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: improving early detection and diagnosis of lung cancer. Biomed Signal Process Control, 2024, 87: 105509.
7. Lu Y Q, Fan X S, Wang J F, et al. PARA-U-net: an improved Unet parallel coding network for lung nodule segmentation. J King Saud Univ Comput Inf Sci, 2024, 36(9): 102203.
8. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation// 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Berlin: Springer, 2015: 234-241.
9. Tong G F, Li Y, Chen H R, et al. Improved U-net network for pulmonary nodules segmentation. Optik, 2018, 174: 460-469.
10. Zhou Z W, Siddiquee M M R, Tajbakhsh N, et al. UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging, 2019, 39(6): 1856-1867.
11. Zhou T, Dong Y L, Lu H L, et al. APU-net: an attention mechanism parallel U-net for lung tumor segmentation. Biomed Res Int, 2022, 2022(1): 5303651.
12. Huang Z Y, Zhao Z Y, Yu Z, et al. SCFMUNet: a fusion architecture based on multi-scale state space model and channel attention for medical image segmentation. Neural Netw, 2025, 192: 107919.
13. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// 31th Advances in Neural Information Processing Systems (NIPS). Long Beach: NIPS, 2017: 5998-6008.
14. Chen J N, Lu Y Y, Yu Q H, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv, 2021: 2102.04306.
15. Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv, 2023: 2312.00752.
16. Liu Y, Tian Y J, Zhao Y Z, et al. VMamba: visual state space model// 37th Advances in Neural Information Processing Systems (NIPS). Vancouver: NIPS, 2024: 103031-103063.
17. Hatamizadeh A, Kautz J. MambaVision: a hybrid Mamba-transformer vision backbone// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2024). New York: IEEE, 2024: 25261-25270.
18. Ruan J C, Li J C, Xiang S C. VM-UNet: vision Mamba Unet for medical image segmentation. arXiv, 2024: 2402.02491.
19. Wu R K, Liu Y H, Liang P C, et al. H-VMUNet: high-order vision Mamba Unet for medical image segmentation. Neurocomputing, 2025, 624: 129447.
20. Dang T D Q, Nguyen H H, Tiulpin A. LoG-VMamba: local-global vision Mamba for medical image segmentation// 17th Asian Conference on Computer Vision (ACCV). Berlin: Springer, 2024: 548-565.
21. Liang P C, Shi L J, Pu B, et al. MambaSAM: a visual Mamba-adapted SAM framework for medical image segmentation. IEEE J Biomed Health Inform, 2025, 29(8): 5824-5835.
22. Xia Q L, Zheng H, Zou H N, et al. A comprehensive review of deep learning for medical image segmentation. Neurocomputing, 2025, 613: 128740.
23. Yao W J, Bai J J, Liao W, et al. From CNN to transformer: a review of medical image segmentation models. J Imaging Inform Med, 2024, 37(4): 1529-1547.
24. Liu J R, Yang H, Zhou H Y, et al. Swin-UMamba?: adapting Mamba-based vision foundation models for medical image segmentation. IEEE Trans Med Imaging, 2024, 44(10): 3898-3908.
25. 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.
26. Zhou H Y, Guo J S, Zhang Y H, et al. nnFormer: volumetric medical image segmentation via a 3D transformer. IEEE Trans Image Process, 2023, 32: 4036-4045.
27. Yang L X, Zhang R Y, Li L D, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks// 13th International Conference on Machine Learning (ICML). New York: ACM, 2021: 11863-11874.
28. Wang Q L, Wu B G, Zhu P F, et al. ECA-net: efficient channel attention for deep convolutional neural networks// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2020). New York: IEEE, 2020: 11534-11542.
29. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2021). New York: IEEE, 2021: 13713-13722.
30. Armato S G, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
31. Müller D, Soto-Rey I, Kramer F. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res Notes, 2022, 15(1): 210.
32. Oktay O, Schlemper J, Le Folgoc L, et al. Attention U-net: learning where to look for the pancreas. arXiv, 2018: 1804.03999.
33. Cao H, Wang Y Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation// European Conference on Computer Vision (ECCV2022). Berlin: Springer, 2022: 205-218.
34. Dumitru R G, Peteleaza D, Craciun C. Using Duck-Net for polyp image segmentation. Sci Rep, 2023, 13(1): 9803.
35. Wang Z Y, Zheng J Q, Zhang Y C, et al. Mamba-UNet: Unet-like pure visual Mamba for medical image segmentation. arXiv, 2024: 2402.05079.
36. Hu J, Shen L, Sun G. Squeeze-and-excitation networks// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2018). New York: IEEE, 2018: 7132-7141.
37. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module// European Conference on Computer Vision (ECCV2022). Berlin: Springer, 2018: 3-19.
38. Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2019). New York: IEEE, 2019: 3146-3154.

Journal of Biomedical Engineering

Lung nodule segmentation method based on multiscale feature interaction and coordinate information

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