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

Authors：

SHANG Peng ,  WANG Yuling

College of Information Engineering, 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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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.

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2.	Zuo Y, Shao J, Razmjooy N. Anterior cruciate ligament tear detection using gated recurrent unit and flexible fitness dependent optimizer. Biomedical Signal Processing and Control, 2024, 96: 106616.
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6.	Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: a survey. IET Image Processing, 2022, 16(5): 1243-1267.
7.	Khalifa A F, Badr E. Deep learning for image segmentation: a focus on medical imaging. Comput Mater Contin, 2023, 75(1): 1995-2024.
8.	Lee H, Hong H, Kim J. Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement. Computers in Biology and Medicine, 2014, 55: 1-10.
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.	Erdur A C, Rusche D, Scholz D, et al. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives. Strahlenther Onkol, 2025, 201(3): 236-254.
11.	Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing, 2022, 506: 311-335.
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.
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15.	孙军梅, 葛青青, 李秀梅, 等. 一种具有边缘增强特点的医学图像分割网络. 电子与信息学报, 2022, 44(5): 1643-1652.
16.	Zhang C, Deng X, Ling S H. Next-Gen medical imaging: U-Net evolution and the rise of transformers. Sensors, 2024, 24(14): 4668.
17.	Flannery S W, Kiapour A M, Edgar D J, et al. Automated magnetic resonance image segmentation of the anterior cruciate ligament. Journal of Orthopaedic Research, 2021, 39(4): 831-840.
18.	Flannery S W, Kiapour A M, Edgar D J, et al. A transfer learning approach for automatic segmentation of the surgically treated anterior cruciate ligament. Journal of Orthopaedic Research, 2022, 40(1): 277-284.
19.	Awan M J, Rahim M S M, Salim N, et al. Automated knee MR images segmentation of anterior cruciate ligament tears. Sensors, 2022, 22(4): 1552.
20.	Dung N T, Thuan N H, Van Dung T, et al. End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI. Diagnostic and Interventional Imaging, 2023, 104(3): 133-141.
21.	Flannery S W, Barnes D A, Costa M Q, et al. Automated segmentation of the healed anterior cruciate ligament from T2* relaxometry MRI scans. Journal of Orthopaedic Research, 2023, 41(3): 649-656.
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.	Gültekin M Z, Dinçel Y M, Keskin Z, et al. Morphometric risk factors effects on anterior cruciate ligament injury. Joint Diseases and Related Surgery, 2023, 34(1): 130-137.
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.	Bien N, Rajpurkar P, Ball R L, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Medicine, 2018, 15(11): e1002699.
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.	Diakogiannis F I, Waldner F, Caccetta P, et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 94-114.
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.

1. Duthon V B, Barea C, Abrassart S, et al. Anatomy of the anterior cruciate ligament. Knee Surg Sports Traumatol Arthrosc, 2006, 14(3): 204-213.
2. Zuo Y, Shao J, Razmjooy N. Anterior cruciate ligament tear detection using gated recurrent unit and flexible fitness dependent optimizer. Biomedical Signal Processing and Control, 2024, 96: 106616.
3. Hewett T E, Di Stasi S L, Myer G D. Current concepts for injury prevention in athletes after anterior cruciate ligament reconstruction. The American Journal of Sports Medicine, 2013, 41(1): 216-224.
4. Kiapour A M, Murray M M. Basic science of anterior cruciate ligament injury and repair. Bone & Joint Research, 2014, 3(2): 20-31.
5. Oakden-Rayner L, Carneiro G, Bessen T, et al. Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 2017, 7(1): 1648.
6. Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: a survey. IET Image Processing, 2022, 16(5): 1243-1267.
7. Khalifa A F, Badr E. Deep learning for image segmentation: a focus on medical imaging. Comput Mater Contin, 2023, 75(1): 1995-2024.
8. Lee H, Hong H, Kim J. Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement. Computers in Biology and Medicine, 2014, 55: 1-10.
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. Erdur A C, Rusche D, Scholz D, et al. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives. Strahlenther Onkol, 2025, 201(3): 236-254.
11. Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing, 2022, 506: 311-335.
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. 孙军梅, 葛青青, 李秀梅, 等. 一种具有边缘增强特点的医学图像分割网络. 电子与信息学报, 2022, 44(5): 1643-1652.
16. Zhang C, Deng X, Ling S H. Next-Gen medical imaging: U-Net evolution and the rise of transformers. Sensors, 2024, 24(14): 4668.
17. Flannery S W, Kiapour A M, Edgar D J, et al. Automated magnetic resonance image segmentation of the anterior cruciate ligament. Journal of Orthopaedic Research, 2021, 39(4): 831-840.
18. Flannery S W, Kiapour A M, Edgar D J, et al. A transfer learning approach for automatic segmentation of the surgically treated anterior cruciate ligament. Journal of Orthopaedic Research, 2022, 40(1): 277-284.
19. Awan M J, Rahim M S M, Salim N, et al. Automated knee MR images segmentation of anterior cruciate ligament tears. Sensors, 2022, 22(4): 1552.
20. Dung N T, Thuan N H, Van Dung T, et al. End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI. Diagnostic and Interventional Imaging, 2023, 104(3): 133-141.
21. Flannery S W, Barnes D A, Costa M Q, et al. Automated segmentation of the healed anterior cruciate ligament from T2* relaxometry MRI scans. Journal of Orthopaedic Research, 2023, 41(3): 649-656.
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. Gültekin M Z, Dinçel Y M, Keskin Z, et al. Morphometric risk factors effects on anterior cruciate ligament injury. Joint Diseases and Related Surgery, 2023, 34(1): 130-137.
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. Bien N, Rajpurkar P, Ball R L, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Medicine, 2018, 15(11): e1002699.
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. Diakogiannis F I, Waldner F, Caccetta P, et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 94-114.
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.

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