A multi-scale feature capturing and spatial position attention model for colorectal polyp image segmentation_Journal of Biomedical Engineering

Authors：

GUO Wen ¹ ,  CHEN Xiangyang ¹ , WU Jian ¹ , LI Jiaqi ² , ZHU Pengxue ³

1. School of Computer Science & Engineering Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, P. R. China;
2. CCCC Fifth Engineering Co., Ltd, Beijing 102308, P. R. China;
3. School of Mathematics & Information, China West Normal University, Nanchong, Sichuan 637001, P. R. China;

Corresponding author：

CHEN Xiangyang, Email: 04005074@wit.edu.cn

Keywords：

Medical image segmentation; Deep learning; Colorectal polyp; Multi-scale feature fusion; Attention mechanism

DOI：

10.7507/1001-5515.202412012

Video：

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Colorectal polyps are important early markers of colorectal cancer, and their early detection is crucial for cancer prevention. Although existing polyp segmentation models have achieved certain results, they still face challenges such as diverse polyp morphology, blurred boundaries, and insufficient feature extraction. To address these issues, this study proposes a parallel coordinate fusion network (PCFNet), aiming to improve the accuracy and robustness of polyp segmentation. PCFNet integrates parallel convolutional modules and a coordinate attention mechanism, enabling the preservation of global feature information while precisely capturing detailed features, thereby effectively segmenting polyps with complex boundaries. Experimental results on Kvasir-SEG and CVC-ClinicDB demonstrate the outstanding performance of PCFNet across multiple metrics. Specifically, on the Kvasir-SEG dataset, PCFNet achieved an F1-score of 0.897 4 and a mean intersection over union (mIoU) of 0.835 8; on the CVC-ClinicDB dataset, it attained an F1-score of 0.939 8 and an mIoU of 0.892 3. Compared with other methods, PCFNet shows significant improvements across all performance metrics, particularly in multi-scale feature fusion and spatial information capture, demonstrating its innovativeness. The proposed method provides a more reliable AI-assisted diagnostic tool for early colorectal cancer screening.

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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. Ta N, Chen H P, Lyu Y, et al. Ble-net: boundary learning and enhancement network for polyp segmentation. Multimedia Systems, 2023, 29(5): 3041-3054.
3. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich: Springer International Publishing, 2015: 234-241.
4. Thanh N C, Long T Q. CRF-EfficientUNet: an improved UNet framework for polyp segmentation in colonoscopy images with combined asymmetric loss function and CRF-RNN layer. IEEE Access, 2021, 9: 156987-157001.
5. Chenarlogh V A, Shabanzadeh A, Oghli M G, et al. Clinical target segmentation using a novel deep neural network: double attention Res-U-Net. Sci Rep, 2022, 12(1): 6717.
6. Kaur A, Kaur L, Singh A. GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets. Neural Computing and Applications, 2021, 33(21): 14991-15025.
7. Jha D, Riegler M A, Johansen D, et al. Doubleu-net: a deep convolutional neural network for medical image segmentation//2020 IEEE 33rd International symposium on computer-based medical systems (CBMS), Rochester: IEEE, 2020: 558-564.
8. Zhong J F, Wang W, Wu H S, et al. PolypSeg: an efficient context-aware network for polyp segmentation from colonoscopy videos//Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima: Springer International Publishing, 2020: 285-294.
9. Zhang W, Fu C, Zheng Y, et al. HSNet: A hybrid semantic network for polyp segmentation. Comput Biol Med, 2022, 150: 106173.
10. Pan S, Liu X, Xie N, et al. EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation. BMC Bioinformatics, 2023, 24(1): 85.
11. Tomar N K, Jha D, Riegler M A, et al. Fanet: a feedback attention network for improved biomedical image segmentation. IEEE Trans Neural Netw Learn Syst, 2022, 34(11): 9375-9388.
12. Shen Y, Jia X, Meng M Q. HRENet: a hard region enhancement network for polyp segmentation//Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg: Springer International Publishing, 2021: 559-568.
13. 孙福艳, 王琼, 吕宗旺, 等. 深度学习在结肠息肉分割中的应用综述. 计算机工程与应用, 2023, 59(23): 15-27.
14. Liu C, Zhang S, Hu M, et al. Object detection in remote sensing images based on adaptive multi-scale feature fusion method. Remote Sensing, 2024, 16(5): 907.
15. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 13713-13722.
16. Ding Z Q, Zhang Y J, Zhu C X, et al. CAT-Unet: an enhanced U-Net architecture with coordinate attention and skip-neighborhood attention transformer for medical image segmentation. Information Sciences, 2024, 670: 120578.
17. Yang Z M, Wu Q L, Zhang F, et al. A new semantic segmentation method for remote sensing images integrating coordinate attention and SPD-Conv. Symmetry, 2023, 15(5): 1037.
18. Jha D, Smedsrud P H, Riegler M A, et al. Kvasir-SEG: a segmented polyp dataset//MultiMedia modeling: 26th international conference (MMM 2020), Daejeon: Springer International Publishing, 2020: 451-462.
19. Bernal J, Sánchez F J, Fernández-Esparrach G, et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph, 2015, 43: 99-111.
20. Chowdhury D, Dey A K, Ghosh K, et al. Retinal vessel segmentation using a novel U-Net architecture with data augmentation//International Conference on Human-Centric Smart Computing, Singapore: Springer Nature Singapore, 2023: 357-372.
21. Diederik P K. Adam: a method for stochastic optimization. arXiv preprint, 2014, arXiv: 1412.6980.
22. Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753.
23. Zhou Z, Siddiquee M M R, 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: 4th International Workshop (DLMIA 2018), Granada: Springer International Publishing, 2018, 11045: 3-11.
24. Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2020, 43(10): 3349-3364.
25. Ates G C, Mohan P, Celik E. Dual cross-attention for medical image segmentation. Engineering Applications of Artificial Intelligence, 2023, 126: 107139.
26. Zuo B, Lee F F, Chen Q. An efficient U-shaped network combined with edge attention module and context pyramid fusion for skin lesion segmentation. Medical & Biological Engineering & Computing, 2022, 60(7): 1987-2000.
27. Raymann J, Rajalakshmi R. GAR-Net: guided attention residual network for polyp segmentation from colonoscopy video frames. Diagnostics, 2022, 13(1): 123.
28. Tomar N K, Srivastava A, Bagci U, et al. Automatic polyp segmentation with multiple kernel dilated convolution network//2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). Shenzen: IEEE, 2022: 317-322.
29. Jha D, Smedsrud P H, Riegler M A, et al. Resunet++: an advanced architecture for medical image segmentation//2019 IEEE international symposium on multimedia (ISM). San Diego: IEEE, 2019: 225-2255.
30. Xu Q, Ma Z C, He N, et al. DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation. Computers in Biology and Medicine, 2023, 154: 106626.
31. Gao Y, Che X, Xu H, et al. An enhanced feature extraction network for medical image segmentation. Applied Sciences, 2023, 13(12): 6977.

Journal of Biomedical Engineering

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