Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention_Journal of Biomedical Engineering

Authors：

SHANG Peng ^1,2 ,  WANG Yuling ^1,2

1. College of Information Engineering, East China University of Technology, Nanchang 330000, P.R. China;
2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and system, East China University of Technology, Nanchang 330000, P.R. China;

Corresponding author：

WANG Yuling, Email: wangyuling_119@vip.163.com

Keywords：

Medical image segmentation; Anterior cruciate ligament; Hybrid attention mechanism; U-Net; Dilated convolution

DOI：

10.7507/1001-5515.202410042

Video：

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Abstract Full text Figures/Tables Video References Cited by

Aiming at the problems of low accuracy and large difference of segmentation boundary distance in anterior cruciate ligament (ACL) image segmentation of knee joint, this paper proposes an ACL image segmentation model by fusing dilated convolution and residual hybrid attention U-shaped network (DRH-UNet). The proposed model builds upon the U-shaped network (U-Net) by incorporating dilated convolutions to expand the receptive field, enabling a better understanding of the contextual relationships within the image. Additionally, a residual hybrid attention block is designed in the skip connections to enhance the expression of critical features in key regions and reduce the semantic gap, thereby improving the representation capability for the ACL area. This study constructs an enhanced annotated ACL dataset based on the publicly available Magnetic Resonance Imaging Network (MRNet) dataset. The proposed method is validated on this dataset, and the experimental results demonstrate that the DRH-UNet model achieves a Dice similarity coefficient (DSC) of (88.01±1.57)% and a Hausdorff distance (HD) of 5.16±0.85, outperforming other ACL segmentation methods. The proposed approach further enhances the segmentation accuracy of ACL, providing valuable assistance for subsequent clinical diagnosis by physicians.

Citation： SHANG Peng, WANG Yuling. Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention. Journal of Biomedical Engineering, 2025, 42(2): 246-254. doi: 10.7507/1001-5515.202410042 Copy

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1.
2.
3.
4.
5.
6.
7.
8.
9. Punitha N, Monapreetha M, Dhanasekar M G R. Active contour segmentation of the anterior cruciate ligament in MR images//2024 10th International Conference on Communication and Signal Processing (ICCSP), Chennai: IEEE, 2024: 491-495.
10.
11.
12. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston: IEEE, 2015: 3431-3440.
13. 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.
14. Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 fourth International Conference on 3D Vision (3DV), Stanford: IEEE, 2016: 565-571.
15.
16.
17.
18.
19.
20.
21.
22. Fisher Y, Vladlen K. Multi-scale context aggregation by dilated convolutions//Proceedings of the International Conference on Learning Representations. San Juan: ICLR, 2016: 674-685.
23.
24. Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV), Munich: ECCV, 2018: 3-19.
25. He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on imagenet classification//Proceedings of the IEEE International Conference on Computer Vision, Santiago: IEEE, 2015: 1026-1034.
26.
27. Chen J, Lu Y, Yu Q, et al. Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
28. Cao H, Wang Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 205-218.
29. Hu J, Shen L, Sun G. Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City: IEEE, 2018: 7132-7141.
30. Wang Q, Wu B, Zhu P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks//Proceedings of the IEEE/CVF CONFERence on Computer Vision and Pattern Recognition, Seattle: IEEE, 2020: 11534-11542.
31. Chen L C. Rethinking atrous convolution for semantic image segmentation. arXiv preprint, 2017, arXiv: 1706.05587.
32.
33. Liao W, Zhu Y, Wang X, et al. LightM-UNet: mamba assists in lightweight unet for medical image segmentation. arXiv preprint, 2024, arXiv: 2403.05246.
34. Ruan J, Li J, Xiang S. VM-UNet: vision mamba unet for medical image segmentation. arXiv preprint, 2024, arXiv: 2402.02491.

Journal of Biomedical Engineering

Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention

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