Medical Image Segmentation Using SegResNet Integrated with Non-local Neural Networks
International Core Journal of Engineering, (2026), Vol.12, No.1, pp.72-79
Published: January 21, 2026
Abstract
Pancreas segmentation is a challenging task in medical image analysis due to factors like ambiguous pancreatic boundaries and inter-individual variability. Current methods need improvement in balancing accuracy and efficiency. This study designs a lightweight pancreas segmentation model based on SegResNet, enhanced with Non-local Neural Networks. Trained on the Task07_Pancreas dataset, the model achieves a Dice coefficient of 91.51% and a Recall rate of 91.97% on the test set, with only 6.3M parameters. It outperforms advanced models such as UMambaEnc and UNETR, effectively balancing segmentation precision and computational efficiency.
Keywords:
Pancreas Segmentation; SegResNet; Medical Image Segmentation; CT Imaging.
APA Citation:
Jun Liao, Jing Zhang, Tingjie He, Yujie Zhang (2026). Medical Image Segmentation Using SegResNet Integrated with Non-local Neural Networks. International Core Journal of Engineering, 12(1), 72-79. https://doi.org/10.6919/ICJE.202601_12(1).0007
References
- R.L. Siegel, K.D. Miller and A. Jemal: Cancer Statistics, 2019, Ca: A Cancer Journal For Clinicians, Vol. 69 (2019) No.1, p.7-34.
- K. Karasawa, M. Oda, T. Kitasaka, et al.: Multi-Atlas Pancreas Segmentation: Atlas Selection Based On Vessel Structure, Medical Image Analysis, Vol. 39 (2017), p.18-28.
- T.D. Tam and N.T. Binh: Efficient Pancreas Segmentation in Computed Tomography Based on Region-Growing, Proc. International Conference on Nature of Computation and Communication (Springer, 2015), p.332-340.
- A. Farag, L. Lu, B. Turkbey, et al.: A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans, Proc. Abdominal Imaging. Computational and Clinical Applications: 6th International Workshop (Springer, 2014), p.103-113.
- O. Ronneberger, P. Fischer and T. Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation, Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), p.234-241.
- O. Oktay, J. Schlemper, L.L. Folgoc, et al.: Attention U-Net: Learning Where to Look for the Pancreas, ArXiv Preprint ArXiv:1804.03999, (2018).
- E.Z. Xie, W.H. Wang, Z.D. Yu, et al.: SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers, Advances in Neural Information Processing Systems, Vol. 34 (2021), p.12077-12090.
- P.R. Liang, G.J. Xin and C.S. Ding: Pancreatic Image Segmentation Method Based on Improved SegFormer, Computer and Modernization, (2025) No.6, p.71-78.
- M. Antonelli, A. Reinke, S. Bakas, et al.: The Medical Segmentation Decathlon, ArXiv Preprint ArXiv:2106.05735, (2021).
- Z.T. Xiang, J.C. Liu, L. Wei, et al.: Pancreatic Image Segmentation Based on Global Feature U-Net, Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), Vol. 34 (2022) No.2, p.216-222.
- J.B. Ji, S. Chen and Y.Y. Yang: Pancreatic CT Segmentation Using Dual Dimensionality Reduction Channel Attention Gated U-Net, Chinese Journal of Biomedical Engineering, Vol. 42 (2023) No.3, p.281-288.
- B.Y. Zhou, G.J. Xin, H. Liang, et al.: Pancreatic Segmentation Based on Two-Stage Multi-Attention Mechanism Network, Computer and Modernization, (2025) No.10, p.67-72.
- H. Ma, Y. Liu and J.R. Zhang: Pancreatic Segmentation Based on Model Compression and Reconstruction U-Net, Computer Engineering and Design, Vol. 43 (2022) No.7, p.1998-2006.
- Information on: https://ai.googleblog.com/2020/12/transformers-for-image-recognitionat.html
- A. Hatamizadeh, Y. Tang, V. Nath, et al.: UNETR: Transformers for 3D Medical Image Segmentation, Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (2022), p.1748–1758.
- A. Myronenko: 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization, Proc. International MICCAI Brainlesion Workshop (Springer, Cham 2018), p.311-320.
- X. Wang, R. Girshick, A. Gupta, et al.: Non-Local Neural Networks, Proc. the IEEE Conference on Computer Vision and Pattern Recognition (2018), p.7794-7803.
- A. Hatamizadeh, V. Nath, Y. Tang, et al.: Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images, Proc. International MICCAI Brainlesion Workshop (Cham: Springer International Publishing, 2021), p.272-284.
- J. Ma, F. Li and B. Wang: U-Mamba: Enhancing Long-Range Dependency for Biomedical Image Segmentation, ArXiv Preprint ArXiv:2401.04722, (2024).