A Comparative Study of 2D and 3D U-Net-Based Architectures for Pancreas and Tumor Segmentation in CT Imaging

The results show that the 3D Res-U-Net achieved the best performance, with a Dice score of 0.81 for pancreas segmentation and 0.51 for tumor segmentation, outperforming both the 2D U-Net and the 3D U-Net.

📌 3 Key Takeaways

🎯

Objective & Hypothesis:Pancreatic cancer ranks among the deadliest forms of cancer, largely due to its asymptomatic progression, late-stage diagnosis, and the pancreas’s deep-seated and anatomically complex location within the abdominal cavity.

📊

Key Findings & Data:Deep learning-based segmentation models, particularly those based on the U-Net architecture, have demonstrated strong potential for the automatic identification of pancreas and pancreatic tumors in computed tomography (CT) images.

💡

Conclusion & Impact:The results show that the 3D Res-U-Net achieved the best performance, with a Dice score of 0.81 for pancreas segmentation and 0.51 for tumor segmentation, outperforming both the 2D U-Net and the 3D U-Net.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:3D U-Net · 3D-Res-U-Net · CT imaging

Abstract

Pancreatic cancer ranks among the deadliest forms of cancer, largely due to its asymptomatic progression, late-stage diagnosis, and the pancreas’s deep-seated and anatomically complex location within the abdominal cavity. These factors collectively prevent early detection, which is critical for improving patient outcomes. Deep learning-based segmentation models, particularly those based on the U-Net architecture, have demonstrated strong potential for the automatic identification of pancreas and pancreatic tumors in computed tomography (CT) images. This study presents a comparative analysis of 2D and 3D U-Net architectures for the segmentation of the pancreas and its tumors. The results show that the 3D Res-U-Net achieved the best performance, with a Dice score of 0.81 for pancreas segmentation and 0.51 for tumor segmentation, outperforming both the 2D U-Net and the 3D U-Net. Additionally, inference time analysis for real-time clinical settings showed that the 2D U-Net enables the fastest predictions, whereas the 3D U-Net and 3D Res-U-Net require significantly higher computation, underscoring key trade-offs for clinical deployment. These findings offer valuable insights into model selection and optimization strategies for real-world clinical applications. The findings underscore the feasibility of developing efficient deep learning-based systems to support early diagnosis and personalized treatment planning for pancreatic cancer.

Keywords

3D U-Net 3D-Res-U-Net CT imaging deep learning medical image segmentation pancreatic cancer U-Net

🔖

Cite This Article

Şen, İ., Çelikten, A., Bingöl, E., Demir, M. K., Akpulat, A., Demirel, S., Budak, A., Karataş, H. (2026). A Comparative Study of 2D and 3D U-Net-Based Architectures for Pancreas and Tumor Segmentation in CT Imaging. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 22 Sayı: 2, 309–319. https://doi.org/10.18466/cbayarfbe.1730380

📄 Full Text PDF Viewer

Open in New Tab ↗

↗New Tab

📄

PDF görüntüleyici hazırlanıyor...

References (26)

.Cancer Research UK. Pancreatic cancer survival. https://www.cancerresearchuk.org/about-cancer/pancreatic-cancer/survival (accessed at 30.04.2025).
. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. (https://doi.org/10.1038/s41592-020-01008-z )
. Christ, P. F., Ettlinger, F., Grün, F., Elshaera, M. E. A., Lipkova, J., Schlecht, S., & Menze, B. (2017). Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint, arXiv:1702.05970.
. Bozkır, Ö. F., Urfalı, A., Çelikten, A., Demirel, S., Budak, A., Karataş, H., & Ceylan, M. (2024). Enhancing aorta segmentation in contrast CT images: A novel deep architectural approach. Osmaniye Korkut Ata University Journal of The Institute of Science and Technology, 7(5), 2284–2303. (https://doi.org/10.47495/okufbed.1457674 )
. Zhang, R., Zheng, Y., Poon, C. C. Y., Shen, D., & Lau, J. Y. W. (2018). Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognition, 83, 209–219.
(https://doi.org/10.1016/j.patcog.2018.05.026) [6]. Jain, S., Sikka, G., & Dhir, R. (2024). A systematic literature review on pancreas segmentation from traditional to non-supervised techniques in abdominal medical images. Artificial Intelligence Review, 57(12), 317. (https://doi.org/10.1007/s10462-024-10966-1)
. Li, M., Lian, F., Li, Y., & Guo, S. (2022). Attention-guided duplex adversarial U-Net for pancreatic segmentation from computed tomography images. Journal of Applied Clinical Medical Physics, 23(4), e13537. (https://doi.org/10.1002/acm2.13537)
. Devendhar, T., Priya, L., & Jayalakshmy, S. (2024). U-Net based pancreas segmentation from computed tomography images. In Proceedings of the 2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), pp. 1–5. IEEE. (https://doi.org/10.1109/ICSTSN61422.2024.10671042)
. Ghorpade, H., Kolhar, S., Jagtap, J., & Chakraborty, J. (2024). An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor. MethodsX, 13, 102995. (https://doi.org/10.1016/j.mex.2024.102995)
. Tualombo, M. E., Reyes, I., Vizcaino-Imacaña, P., & Morocho-Cayamcela, M. E. (2024). Pancreas segmentation using SRGAN combined with U-Net neural network. In Conference on Information and Communication Technologies of Ecuador, pp. 21–38. Springer Nature Switzerland. (https://doi.org/10.1007/978-3-031-75431-9_2)
. Bayram, A. F., Gürkan, C., Budak, A., & Karataş, H. (2022). Fully automatic end-to-end convolutional neural networks-based pancreatic tumor segmentation on CT modality. Turkish Journal of Forecasting, 6(2), 67–72. (https://doi.org/10.34110/forecasting.1190299)
. Derin, A., Gürkan, C., Budak, A., & Karataş, H. (2022). Pancreas segmentation using U-Net based segmentation networks in CT modality: A comparative analysis. European Journal of Science and Technology, 40, 94–98. (https://doi.org/10.31590/ejosat.1171803)
. Chen, L., & Wan, L. (2023). CTUNet: Automatic pancreas segmentation using a channel-wise transformer and 3D U-Net. The Visual Computer, 39(11), 5229–5243. (https://doi.org/10.1007/s00371-022-02656-2)
. Uslucuk, A., & Öcal, H. (2023). Comparative analysis of baseline VNet and UNet architectures on pancreas segmentation. Orclever Proceedings of Research and Development, 3(1), 146–157. (https://doi.org/10.56038/oprd.v3i1.309)
. Ghorpade, H., Jagtap, J., Patil, S., Kotecha, K., Abraham, A., Horvat, N., & Chakraborty, J. (2023). Automatic segmentation of pancreas and pancreatic tumor: A review of a decade of research. IEEE Access, 11, 108727–108745. (https://doi.org/10.1109/ACCESS.2023.3320570)
. Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., & Cardoso, M. J. (2022). The medical segmentation decathlon. Nature Communications, 13(1), 4128. (https://doi.org/10.1038/s41467-022-30695-9)
. Ji, Y., Bai, H., Yang, J., Ge, C., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., Wan, X., & Luo, P. (2022). AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Advances in Neural Information Processing Systems, 35, 36722–36732.(https://arxiv.org/abs/2206.08023)
. Wasserthal, J., Breit, H. C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., & Segeroth, M. (2023). TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence, 5(5), e230024.(https://doi.org/10.1148/ryai.230024)
. Zhang, U. (2021). Pancreas cancer segmentation on portal-venous phase CTs [Dataset]. Zenodo. (https://doi.org/10.5281/zenodo.5510650)
. Ma, J., et al. (2021). FLARE 2021: Fast and low-resource abdominal organ segmentation challenge [Dataset]. Grand Challenge. https://flare.grand-challenge.org/ (accessed at 23.06.2025).
. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241. Springer International Publishing. (https://doi.org/10.1007/978-3-319-24574-4_28)
. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, pp. 424–432. Springer International Publishing. (https://doi.org/10.1007/978-3-319-46723-8_49)
. Fu, W., Ma, Z., Yang, Z., Yu, S., Zhang, Y., Zhang, X., Mei, B., Meng, Y., Ma, C., & Gong, X. (2025). Fully automated image quality assessment based on deep learning for carotid computed tomography angiography: A multicenter study. Physica Medica, 134, 104990. (https://doi.org/10.1016/j.ejmp.2025.104990)
. Wang, J., Zhang, J. H., Zhang, J. L., Lu, F. M., Meng, R. G., & Wang, Z. (2021). Research on fault recognition method combining 3D Res-UNet and knowledge distillation. Applied Geophysics, 18(2), 199–212. (https://doi.org/10.1007/s11770-021-0894-2)
. Liu, J., Yildirim, O., Akin, O., & Tian, Y. (2023). AI-driven robust kidney and renal mass segmentation and classification on 3D CT images. Bioengineering, 10(1), 116. (https://doi.org/10.3390/bioengineering10010116)
. Cao, L., & Li, J. (2024). Strongly representative semantic-guided segmentation network for pancreatic and pancreatic tumors. Biomedical Signal Processing and Control, 87, 105562. (https://doi.org/10.1016/j.bspc.2023.105562)