Pancreas segmentation with multi-channel convolution and combined deep supervision_Journal of Biomedical Engineering

Authors：

YANG Yue ,  WANG Yongxiong , QIN Chendong

School of Opto-Electronic Information & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P. R. China;

Corresponding author：

WANG Yongxiong, Email: 1439240191@qq.com

Keywords：

Multi-channel convolution; Ensemble decoder; Deep supervision; Contrastive loss; Pancreas segmentation

DOI：

10.7507/1001-5515.202409019

Video：

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Abstract Full text Figures/Tables Video References Cited by

Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural network (CNN) and Transformer-based networks perform well but have limitations: CNN have constrained receptive fields, and Transformer underutilize image features. This work proposes an improved pancreas segmentation method by combining CNN and Transformer. Point-wise separable convolution was introduced in a stage-wise encoder to extract more features with fewer parameters. A densely connected ensemble decoder enabled multi-scale feature fusion, addressing the structural constraints of skip connections. Consistency terms and contrastive loss were integrated into deep supervision to ensure model accuracy. Extensive experiments on the Changhai and National Institute of Health (NIH) pancreas datasets achieved the highest Dice similarity coefficient (DSC) values of 76.32% and 86.78%, with superiority in other metrics. Ablation studies validated each component’s contributions to performance and parameter reduction. Results demonstrate that the proposed loss function smooths training and optimizes performance. Overall, the method outperforms other advanced methods, enhances pancreas segmentation performance, supports physician diagnosis, and provides a reliable reference for future research.

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1. Onneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Cham: Springer International Publishing, 2015: 234-241.
2. Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett, 2018, 15(5): 749-753.
3. Zhou Z, 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.
4. 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.
5. 秦辰栋, 王永雄, 张佳鹏. 基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络. 计算机应用研究, 2024, 41(4): 1264-1269.
6. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// 31st Conference on Neural Information Processing Systems (NIPS2017). Long Beach: NIPS, 2017: 6000-6010.
7. Liu Z, Lin Y, Cao Y, et al. Swin Transformer: Hierarchical vision Transformer using shifted windows// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 10012-10022.
8. Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin Transformer for semantic segmentation of brain tumors in MRI images// International MICCAI Brainlesion Workshop. Cham: Springer International Publishing, 2021: 272-284.
9. 周志, 张孙杰, 张晓玥. 一种改进U型神经网络的医学细胞核图像分割方法. 小型微型计算机系统, 2023, 44(1): 110-116.
10. 孙红, 朱江明, 吴一凡, 等. GFENet: 基于Transformer的高效医学图像分割网络. 小型微型计算机系统, 2024, 45(7): 1728-1733.
11. He Y, Nath V, Yang D, et al. Swinunetr-v2: Stronger swin Transformers with stagewise convolutions for 3D medical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 416-426.
12. Oktay O. Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv, 2018: 1804.03999.
13. Zeng G, Zheng G. Holistic decomposition convolution for effective semantic segmentation of medical volume images. Med Image Anal, 2019, 57: 149-164.
14. Chollet F. Xception: Deep learning with depthwise separable convolutions// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 1251-1258.
15. Wang W, Xie E, Li X, et al. Pyramid vision Transformer: A versatile backbone for dense prediction without convolutions// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE 2021: 568-578.
16. 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 preprint arXiv, 2022: 2091.15076.
17. Wen X W, Chen C, Meng D, et al. Transbts: Multimodal brain tumor segmentation using Transformer// International Conference on Medical Image Computing and Computer-Assisted Intervention. Strasbourg: Springer. 2021: 109-119.
18. Zhou H Y, Guo J, Zhang Y, et al. nnFormer: Volumetric medical image segmentation via a 3D Transformer. IEEE Trans Image Process, 2023, 32: 4036-4045.
19. Li J, Lin X, Che H, et al. Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet. Phys Med Biol, 2021, 66(11): 115010.
20. Wu Y, Xu M, Ge Z, et al. Semi-supervised left atrium segmentation with mutual consistency training// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2021. Cham: Springer International Publishing, 2021: 297-306.
21. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708.
22. Li H, Reichert M, Lin K, et al. Differential diagnosis for pancreatic cysts in CT scans using densely-connected convolutional networks// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 2095-2098.
23. Zhang J, Zhang Y, Jin Y, et al. MDU-Net: Multi-scale densely connected U-Net for biomedical image segmentation. Health Inf Sci Syst, 2023, 11(1): 13.
24. Huang H, Lin L, Tong R, et al. UNet 3+: A full-scale connected UNet for medical image segmentation// IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Bracelona: IEEE, 2020: 1055-1059.
25. Lee C Y, Xie S, Gallagher P, et al. Deeply-supervised nets// Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. JMLR, 2015, 38: 562-570.
26. Arrastia J, Heilenkötter N, Otero B D, et al. Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of basal cell carcinoma. J Imaging, 2021, 7(4): 71.
27. Zhou Y, Xie L, Fishman E K, et al. Deep supervision for pancreatic cyst segmentation in abdominal CT scans// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 2017: 222-230.

Journal of Biomedical Engineering

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