MSCNet: Coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement_Journal of Biomedical Engineering

Authors：

ZENG An ¹ , CHENG Xianhang ¹ ,  PAN Dan ^2,3 , YE Jiayu ¹

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, P. R. China;
2. School of Electronics and Information Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, P. R. China;
3. School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, United Kingdom;

Corresponding?author：

PAN Dan, Email: pandan@gpnu.edu.cn

Keywords：

Coronary artery disease; Segmentation; Multi-scale cascade; Large kernel convolution; Spatial frequency matrix; Re-parameterization

DOI：

10.7507/1001-5515.202407087

Video：

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Abstract Full text Figures/Tables Video References Cited by

Coronary artery segmentation is a critical step in the clinical diagnosis of coronary heart disease. To tackle segmentation breaks and false segmentation caused by the thin and complex coronary vessels as well as the severe foreground-background imbalance in computed tomography angiography images, this paper proposes MSCNet, a coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement. The network constructed a multi-scale cascaded encoder using Swin Transformer and large-kernel convolutions. It sequentially modeled and fused multi-scale features by capturing long-range dependencies and local details, and reparameterized large-kernel convolutions via a spatial frequency matrix to strengthen fine detail capture. Meanwhile, a spatial transformer module was designed to dynamically guide multi-head attention learning and optimize decoding performance. On the ImageCAS dataset, MSCNet achieved an average Dice coefficient of 81.24%, which was 3.57%, 3.78%, and 3.85% higher than 3D UX-Net, SwinUNETR, and SegMamba, respectively. MSCNet effectively improves the accuracy of coronary artery segmentation and provides support for clinical evaluation.

Citation： ZENG An, CHENG Xianhang, PAN Dan, YE Jiayu. MSCNet: Coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement. Journal of Biomedical Engineering, 2026, 43(3): 479-486. doi: 10.7507/1001-5515.202407087 Copy

1.	袁嘉俊, 袁明遠, 張佳胤. 人工智能在冠狀動脈CT血管成像中的應用與進展. 中國醫刊, 2023, 58(5): 468-472.
2.	曾安, 吳春彪, 徐小維, 等. 基于多尺度集成的冠狀動脈分割模型. 生物醫學工程研究, 2022, 41(3): 239-247.
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4.	Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-net. IEEE Geosci Remote Sens Lett, 2018, 15(5): 749-753.
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6.	Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: a nested U-net architecture for medical image segmentation//Stoyanov D, Taylor Z, Carneiro G, et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA ML-CDS 2018. Lecture Notes in Computer Science. Cham: Springer, 2018, 11045: 3-11.
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8.	Shen Y, Fang Z, et al. Coronary arteries segmentation based on 3D FCN with attention gate and level set function. IEEE Access, 2019, 7: 42826-42835.
9.	Wolterink J M, Leiner T, I?gum I. Graph convolutional networks for coronary artery segmentation in cardiac CT angiography//Zhang Daoqiang, Zhou Luping, Jie Biao, et al. 1st International Workshop on Graph Learning in Medical Imaging. Lecture Notes in Computer Science. Cham: Springer, 2019, 11849: 62-69.
10.	Lee H H, Bao S, Huo Y, et al. 3D UX-Net: a large kernel volumetric ConvNet modernizing hierarchical transformer for medical image segmentation. arXiv, 2022: 2209.15076.
11.	Ding X, Zhang X, Han J, et al. Scaling up your kernels to 31x31: revisiting large kernel design in CNNs//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 11963-11975.
12.	Lee H H, Liu Q, Yang Q, et al. DeformUX-Net: exploring a 3D foundation backbone for medical image segmentation with depthwise deformable convolution. arXiv, 2023: 2310.00199.
13.	Chen J, Lu Y, Yu Q, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv, 2021: 2102.04306.
14.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//2017 International Conference on Neural Information Processing Systems (NIPS). Long Beach: Curran Associates, 2017: 5998-6008.
15.	Liu Z, Lin Y, Cao Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021: 10012-10022.
16.	Hatamizadeh A, Tang Y, Nath V, et al. UNETR: transformers for 3D medical image segmentation//2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: IEEE, 2022: 574-584.
17.	Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images//Crimi A, Bakas S. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science. Cham: Springer, 2021, 12962: 272-284.
18.	Ding X, Zhang Y, Ge Y, et al. UniRepLKNet: a universal perception large-kernel ConvNet for audio, video, point cloud, time-series and image recognition. arXiv, 2023: 2311.15599.
19.	Zeng A, Wu C, Lin G, et al. ImageCAS: a large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comput Med Imaging Graph, 2023, 109: 102287.
20.	Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization//Crimi A, Bakas S, Kuijf H, et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science. Cham: Springer, 2019, 11384: 311-320.
21.	Zhou H Y, Guo J, Zhang Y, et al. nnFormer: interleaved transformer for volumetric segmentation. arXiv, 2021: 2109.03201.
22.	Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv, 2023: 2312.00752.
23.	Xing Z, Ye T, Yang Y, et al. SegMamba: long-range sequential modeling Mamba for 3D medical image segmentation. arXiv, 2024: 2401.13560.

1. 袁嘉俊, 袁明遠, 張佳胤. 人工智能在冠狀動脈CT血管成像中的應用與進展. 中國醫刊, 2023, 58(5): 468-472.
2. 曾安, 吳春彪, 徐小維, 等. 基于多尺度集成的冠狀動脈分割模型. 生物醫學工程研究, 2022, 41(3): 239-247.
3. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//Navab N, Hornegger J, Wells W, et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. Cham: Springer, 2015, 9351: 234-241.
4. Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-net. IEEE Geosci Remote Sens Lett, 2018, 15(5): 749-753.
5. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 4700-4708.
6. Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: a nested U-net architecture for medical image segmentation//Stoyanov D, Taylor Z, Carneiro G, et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA ML-CDS 2018. Lecture Notes in Computer Science. Cham: Springer, 2018, 11045: 3-11.
7. ?i?ek ?, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Ourselin S, Joskowicz L, Sabuncu M, et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Lecture Notes in Computer Science. Cham: Springer, 2016, 9901: 424-432.
8. Shen Y, Fang Z, et al. Coronary arteries segmentation based on 3D FCN with attention gate and level set function. IEEE Access, 2019, 7: 42826-42835.
9. Wolterink J M, Leiner T, I?gum I. Graph convolutional networks for coronary artery segmentation in cardiac CT angiography//Zhang Daoqiang, Zhou Luping, Jie Biao, et al. 1st International Workshop on Graph Learning in Medical Imaging. Lecture Notes in Computer Science. Cham: Springer, 2019, 11849: 62-69.
10. Lee H H, Bao S, Huo Y, et al. 3D UX-Net: a large kernel volumetric ConvNet modernizing hierarchical transformer for medical image segmentation. arXiv, 2022: 2209.15076.
11. Ding X, Zhang X, Han J, et al. Scaling up your kernels to 31x31: revisiting large kernel design in CNNs//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 11963-11975.
12. Lee H H, Liu Q, Yang Q, et al. DeformUX-Net: exploring a 3D foundation backbone for medical image segmentation with depthwise deformable convolution. arXiv, 2023: 2310.00199.
13. Chen J, Lu Y, Yu Q, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv, 2021: 2102.04306.
14. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//2017 International Conference on Neural Information Processing Systems (NIPS). Long Beach: Curran Associates, 2017: 5998-6008.
15. Liu Z, Lin Y, Cao Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021: 10012-10022.
16. Hatamizadeh A, Tang Y, Nath V, et al. UNETR: transformers for 3D medical image segmentation//2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: IEEE, 2022: 574-584.
17. Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images//Crimi A, Bakas S. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science. Cham: Springer, 2021, 12962: 272-284.
18. Ding X, Zhang Y, Ge Y, et al. UniRepLKNet: a universal perception large-kernel ConvNet for audio, video, point cloud, time-series and image recognition. arXiv, 2023: 2311.15599.
19. Zeng A, Wu C, Lin G, et al. ImageCAS: a large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comput Med Imaging Graph, 2023, 109: 102287.
20. Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization//Crimi A, Bakas S, Kuijf H, et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science. Cham: Springer, 2019, 11384: 311-320.
21. Zhou H Y, Guo J, Zhang Y, et al. nnFormer: interleaved transformer for volumetric segmentation. arXiv, 2021: 2109.03201.
22. Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv, 2023: 2312.00752.
23. Xing Z, Ye T, Yang Y, et al. SegMamba: long-range sequential modeling Mamba for 3D medical image segmentation. arXiv, 2024: 2401.13560.

Journal of Biomedical Engineering

MSCNet: Coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement

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