A Novel Optimization of the ResU-Net Model for High Precision Multi-Organ Segmentation

Abstract

The precise identification and segmentation are indispensable for effective therapy planning and complete surgi cal management in medical imaging. However, conventional segmentation methods recurrently encounter chal lenges due to the intrinsic complexity of anatomical structures, variability in organ structure, and inconsistency across diverse imaging modalities. This study employs an optimized hybrid deep learning approach that inte grates ResNet50 with an attention-augmented UNet architecture to improve segmentation accuracy and organ localization in medical imaging. The ResNet50 model uses an encoder that focuses on deep feature extraction, whereas the UNet serves as the decoder and is enhanced with an attention mechanism. They are both integrated to enhance the model’s efficiency by capturing both the global framework and local spatial details through a hy brid structure and skip connections, which improve segmentation performance. The proposed model was trained on a large-scale multi-organ dataset of high-resolution MRI to ensure robustness. In addition, the dataset was augmented and regularized to stabilize the model. The major performance metrics, namely Intersection over Union (IoU) and Dice coefficient, indicate that the proposed scheme achieves a high segmentation precision of up to 98.41%. These outcomes indicate that the model is highly feasible for deployment in clinical workflows to improve multi-organ detection with precise performance.

Keywords

• Deep learning
• Hybrid segmentation
• Medical image segmentation
• Multi-organ segmentation
• ResU-Net
• U-Net

References

1. Q. Wan, Z. Yan, and L. Yu, “FedIOD: Federated multi-organ segmentation from partial labels by exploring inter-organ dependency,” IEEE J. Biomed. Health Inform., vol. 28, no. 7, pp. 4105–4117, Jul. 2024. doi: 10.1109/JBHI.2024.3381844
2. Z. Feng, L. Wen, B. Yan, J. Cui, and Y. Wang, “Alleviating class imbalance in semi-supervised multi-organ segmentation via balanced subclass regularization,” IEEE Signal Process. Lett., vol. 31, pp. 2450–2454, 2024. doi: 10.1109/LSP.2024.3451962
3. J. Ding, W. Ni, J. Wan, X. Deng, and L. Wan, “MulA-nnUNet: A multi-attention enhanced nnUNet framework for 3D abdominal multi-organ segmentation,” IEEE Access, vol. 12, pp. 106658–106671, Aug. 2024. doi: 10.1109/ACCESS.2024.3437652
4. Y. Shao, K. Zhou, and L. Zhang, “CSSNet: Cascaded spatial shift network for multi-organ segmentation,” Comput. Biol. Med., vol. 170, Art. no. 107955, Mar. 2024. doi: 10.1016/j.compbiomed.2024.107955
5. Z. Chen et al., “Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation,” Sci. Rep., vol. 14, Art. no. 9784, Apr. 2024. doi: 10.1038/s41598-024-60668-5
6. Q. Zhao et al., “Efficient multi-organ segmentation from 3D abdominal CT images with lightweight network and knowledge distillation,” IEEE Trans. Med. Imaging, vol. 42, no. 9, pp. 2513–2523, Sep. 2023. doi: 10.1109/TMI.2023.3262680
7. S. Irshad, D. P. S. Gomes, and S. T. Kim, “Improved abdominal multi-organ segmentation via 3D boundary-constrained deep neural networks,” IEEE Access, vol. 11, pp. 35097–35110, 2023. doi: 10.1109/ACCESS.2023.3264582
8. X. Li et al., “SUnet: A multi-organ segmentation network based on multiple attention,” Comput. Biol. Med., vol. 167, Art. no. 107596, Dec. 2023. doi: 10.1016/j.compbiomed.2023.107596

9. S. Pan et al., “Abdomen CT multi-organ segmentation using token-based MLP-Mixer,” Med. Phys., vol. 50, no. 5, pp. 3027–3038, May 2023. doi: 10.1002/mp.16135
10. S. Lian, L. Li, Z. Luo, Z. Zhong, B. Wang, and S. Li, “Learning multi-organ segmentation via partial- and mutual-prior from single-organ datasets,” Biomed. Signal Process. Control, vol. 80, Art. no. 104334, Feb. 2023. doi: 10.1016/j.bspc.2022.104339
11. Y. Zhao, J. Li, and Z. Hua, “MPSHT: Multiple progressive sampling hybrid model multi-organ segmentation,” IEEE J. Transl. Eng. Health Med., vol. 10, Art. no. 1800909, 2022. doi: 10.1109/JTEHM.2022.3210047
12. Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu, and X. Yang, “A review of deep learning based methods for medical image multi-organ segmentation,” Phys. Med., vol. 85, pp. 107–122, May 2021. doi: 10.1016/j.ejmp.2021.05.003
13. P.-H. Conze et al., “Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks,” Artif. Intell. Med., vol. 117, Art. no. 102109, Jul. 2021. doi; 10.1016/j.artmed.2021.102109
14. G. Shi, L. Xiao, Y. Chen, and S. K. Zhou, “Marginal loss and exclusion loss for partially supervised multi-organ segmentation,” Med. Image Anal., vol. 70, Art. no. 101979, May 2021. doi;10.1016/j.media.2021.101979
15. H. Lin, Z. Li, Z. Yang, and Y. Wang, “Variance-aware attention U-Net for multi-organ segmentation,” Med. Phys., vol. 48, no. 12, pp. 7864–7876, Dec. 2021. doi: 10.1002/mp.15322
16. N. Kumar et al., “A multi-organ nucleus segmentation challenge,” IEEE Trans. Med. Imaging, vol. 39, no. 5, pp. 1380–1391, May 2020. doi: 10.1109/TMI.2019.2947628.
17. L. Zhang et al., “Block-level skip connections across cascaded V-Net for multi-organ segmentation,” IEEE Trans. Med. Imaging, vol. 39, no. 9, pp. 2782–2793, Sep. 2020. doi: 10.1109/TMI.2020.2975347
18. X. Fang and P. Yan, “Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,” IEEE Trans. Med. Imaging, vol. 39, no. 11, pp. 3619–3629, Nov. 2020. doi: 10.1109/TMI.2020.3001036
19. S. Liang, K.-H. Thung, D. Nie, Y. Zhang, and D. Shen, “Multi-view spatial aggregation framework for joint localization and segmentation of organs at risk in head and neck CT images,” IEEE Trans. Med. Imaging, vol. 39, no. 9, pp. 2794–2805, Sep. 2020. doi: 10.1109/TMI.2020.2975853
20. A. Sharmin, D. Y. Robinson, and J. Song, “Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network,” J. Med. Imag., vol. 7, no. 5, Art. no. 055001, Sep. 2020. doi: 10.1117/1.JMI.7.5.055001
21. B. Rister, D. Yi, K. Shivakumar, T. Nobashi, and D. L. Rubin, “CT-ORG, a new dataset for multiple organ segmentation in computed tomography,” Sci. Data, vol. 7, Art. no. 381, Nov. 2020. doi: 10.1038/s41597-020-00715-8
22. Y. Liu et al., “CT-based multi-organ segmentation using a 3D self-attention U-Net network for pancreatic radiotherapy,” Med. Phys., vol. 47, no. 9, pp. 4316–4324, Sep. 2020. doi: 10.1002/mp.14386
23. O. Schoppe et al., “Deep learning-enabled multi-organ segmentation in whole-body mouse scans,” Nat. Commun., vol. 11, Art. no. 5626, Nov. 2020. doi: 10.1038/s41467-020-19449-7
24. K. He, X. Cao, Y. Shi, D. Nie, Y. Gao, and D. Shen, “Pelvic organ segmentation using distinctive curve guide fully convolutional networks,” IEEE Trans. Med. Imaging, vol. 38, no. 2, pp. 585–595, Feb. 2019. doi: 10.1109/TMI.2018.2867837
25. Y. Wang, Y. Zhou, W. Shen, S. Park, E. K. Fishman, and A. L. Yuille, “Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,” Med. Image Anal., vol. 55, pp. 88–102, Jul. 2019. doi; 10.1016/j.media.2019.04.005
26. S. A. Taghanaki et al., “Combo loss: Handling input and output imbalance in multi-organ segmentation,” Comput. Med. Imag. Graph., vol. 75, pp. 24–33, Jul. 2019. doi: 10.1016/j.compmedimag.2019.04.005
27. E. Tappeiner et al., “Multi-organ segmentation of the head and neck area: An efficient hierarchical neural networks approach,” Int. J. Comput. Assist. Radiol. Surg., vol. 14, no. 5, pp. 745–754, May 2019. doi: 10.1007/s11548-019-01922-4
28. S. Li, H. Jiang, Y.-D. Yao, and B. Yang, “Organ location determination and contour sparse representation for multi-organ segmentation,” IEEE J. Biomed. Health Inform., vol. 22, no. 3, pp. 852–861, May 2018. doi: 10.1109/JBHI.2017.2705037
29. E. Gibson et al., “Automatic multi-organ segmentation on abdominal CT with dense V-networks,” IEEE Trans. Med. Imaging, vol. 37, no. 8, pp. 1822–1834, Aug. 2018. doi: 10.1109/TMI.2018.2806309
30. P. Hu, F. Wu, J. Peng, Y. Bao, F. Chen, and D. Kong, “Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets,” Int. J. Comput. Assist. Radiol. Surg., vol. 12, no. 3, pp. 399–411, Mar. 2017. doi: 10.1007/s11548-016-1501-5.
31. “UW-Madison GI tract image segmentation,” Kaggle, 2022. [Online]. Available: Kaggle. Accessed: Nov. 27, 2024.
32. Y. Gao, Y. Shao, J. Lian, A. Z. Wang, R. C. Chen, and D. Shen, “Accurate segmentation of CT male pelvic organs via regression-based deformable models and multi-task random forests,” IEEE Trans. Med. Imaging, vol. 35, no. 6, pp. 1532–1543, Jun. 2016. doi: 10.1109/TMI.2016.2519264
33. T. Tong et al., “Discriminative dictionary learning for abdominal multi-organ segmentation,” Med. Image Anal., vol. 23, no. 1, pp. 92–104, Jul. 2015. doi; 10.1016/j.media.2015.04.015
34. T. Okada, M. G. Linguraru, M. Hori, R. M. Summers, N. Tomiyama, and Y. Sato, “Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors,” Med. Image Anal., vol. 26, no. 1, pp. 1–18, Dec. 2015. doi; 10.1016/j.media.2015.06.009
35. R. Wolz, C. Chu, K. Misawa, M. Fujiwara, K. Mori, and D. Rueckert, “Automated abdominal multi-organ segmentation with subject-specific atlas generation,” IEEE Trans. Med. Imaging, vol. 32, no. 9, pp. 1723–1730, Sep. 2013. doi: 10.1109/TMI.2013.2265805
36. N. Lay, N. Birkbeck, J. Zhang, and S. K. Zhou, “Rapid multi-organ segmentation using context integration and discriminative models,” in Proc. Inf. Process. Med. Imaging (IPMI), 2013, pp. 450–462. doi: 10.1007/978-3-642-38868-2_38
37. M. Suzuki, M. G. Linguraru, and K. Okada, “Multi-organ segmentation with missing organs in abdominal CT images,” in Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), 2012, pp. 418–425. doi: 10.1007/978-3-642-33454-2_52
38. T. Kohlberger et al., “Automatic multi-organ segmentation using learning-based segmentation and level set optimization,” in Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), 2011, pp. 338–345. doi: 10.1007/978-3-642-23626-6_42
39. T. Okada, K. Yokota, M. Hori, M. Nakamoto, H. Nakamura, and Y. Sato, “Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images,” in Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), 2008, pp. 502–509. doi: 10.1007/978-3-540-85988-8_60