Development of a Gastrointestinal Medical Image Segmentation Model Based on Transformer Networks

Authors

  • Nafiseh Zamani Faculty of Electrical and Computer Engineering, Mazandaran University of Science and Technology, Behshahr, Iran.
  • Reza Javanmard Alitappeh * Faculty of Electrical and Computer Engineering, Mazandaran University of Science and Technology, Behshahr, Iran.

https://doi.org/10.22105/ahse.vi.69

Abstract

Segmentation of the gastrointestinal tract in MRI images is of great importance for identifying and distinguishing organs from tumors, and its goal is to improve the accuracy and speed of precise diagnosis of gastrointestinal regions in medical images. This process typically requires considerable effort and time, as physicians must manually identify the exact location of the stomach and intestines. To address this challenge, we use advanced deep learning methods. This model can help physicians accurately identify gastrointestinal organs. In this way, this method can provide significant improvement in the treatment of gastrointestinal cancer and the analysis of medical images. In this article, five widely used models in medical image segmentation have been reviewed and compared. These models have been analyzed in terms of architecture, performance, number of parameters, and evaluation metrics. The results show that each of these models has specific advantages and limitations, and the selection of the appropriate model depends on the specific needs of each application. The best model, UNet, achieved a Dice score of 0.88.    

Keywords:

Gastrointestinal image segmentation, Deep learning, Evaluation metrics, Advanced deep learning methods

References

  1. [1] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … ., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
  2. [2] Bernal, J., Tajkbaksh, N., Sánchez, F. J., Matuszewski, B. J., Chen, H., Yu, L., … ., & Histace, A. (2017). Comparative validation of polyp detection methods in video colonoscopy: Results from the MICCAI 2015 endoscopic vision challenge. IEEE Transactions on Medical Imaging, 36(6), 1231–1249. https://doi.org/10.1109/TMI.2017.2664042
  3. [3] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  4. [4] Gonzalez, R. C. (2009). Digital image processing. Pearson education india.
  5. [5] Sonka, M., Hlavac, V., & Boyle, R. (2013). Image processing, analysis and machine vision. Springer. https://doi.org/10.1007/978-1-4899-3216-7
  6. [6] Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331. https://doi.org/10.1007/BF00133570
  7. [7] Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
  8. [8] Canny, J. F. (1986). A computation approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6), 670–700. https://cir.nii.ac.jp/crid/1570009750141232256
  9. [9] Sobel, I., & Feldman, G. (1973). A 3×3 isotropic gradient operator for image processing. https://www.researchgate.net/publication/285159837
  10. [10] Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2(1), 315–337. https://doi.org/10.1146/annurev.bioeng.2.1.315
  11. [11] Bankman, I. (2008). Handbook of medical image processing and analysis. Elsevier. https://doi.org/10.1016/B978-0-12-373904-9.X0001-4
  12. [12] Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D u-net: Learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer-Assisted Intervention -- Miccai 2016 (pp. 424–432). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46723-8_49%0A%0A
  13. [13] Oktay, O., Schlemper, J., Folgoc, L. Le, Lee, M., Heinrich, M., Misawa, K., … ., & Rueckert., D. (2018). Attention U-Net: Learning where to look for the pancreas. https://doi.org/10.48550/arXiv.1804.03999
  14. [14] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention -- Miccai 2015 (pp. 234–241). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28%0A%0A
  15. [15] Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3–11). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-00889-5_1%0A%0A
  16. [16] Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. https://doi.org/10.48550/arXiv.1706.05587
  17. [17] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE international conference on computer vision (pp. 2961–2969). IEEE.
  18. [18] Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117–2125). IEEE.
  19. [19] Chaurasia, A., & Culurciello, E. (2017). LinkNet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP) (pp. 1–4). IEEE. https://doi.org/10.1109/VCIP.2017.8305148
  20. [20] Kumar Tiwary, P., Johri, P., Katiyar, A., & Chhipa, M. K. (2025). Deep learning-based MRI brain tumor segmentation with efficientnet-enhanced UNet. IEEE Access, 13, 54920–54937. https://doi.org/10.1109/ACCESS.2025.3554405
  21. [21] Neware, G., & Poulose, A. (2025). Advancing gastrointestinal tract cancer treatment: A comparative study of state-of-the-art deep learning models for mri segmentation. 2025 Emerging Technologies for Intelligent Systems (ETIS) (pp. 1–6). IEEE. https://doi.org/10.1109/ETIS64005.2025.10961070
  22. [22] Kaggle. (2022). UW-madison GI tract image segmentation. https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation/data
  23. [23] Şener, A., & Ergen, B. (2025). Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods. Health Information Science and Systems, 13(1), 37. https://doi.org/3755-025-00354-6

Published

2026-07-12

Issue

Articles in Press

Section

Articles

How to Cite

Zamani, N., & Javanmard Alitappeh, R. (2026). Development of a Gastrointestinal Medical Image Segmentation Model Based on Transformer Networks. Annals of Healthcare Systems Engineering. https://doi.org/10.22105/ahse.vi.69

Similar Articles

1-10 of 31

You may also start an advanced similarity search for this article.