|
Enhancing medical image segmentation accuracy using a hybrid U-Net and ResNet18 architecture for improved treatment planning
|
Hajar Ahmadi1    , Azimeh NV Dehkordi *2 , Farhad Azimifar3 , Seied Rabi Mehdi Mahdavi4 , Mahnaz Roayaei5 |
1- Department¬ of Biomedical Engineering, Isf.C., Islamic Azad University, Isfahan, Iran 2 Department of Physics, Na.C., Islamic Azad University, Najafabad, Iran
2- Assistant Professor, Department of Biomedical Engineering, Isf.C., Islamic Azad University, Isfahan, Iran, , nourizadeh@iau.ac.ir
3- Assistant Professor, Department of Biomedical Engineering, Isf.C., Islamic Azad University, Isfahan, Iran,
4- Department of Medical Physics and Radio-Oncology, Faculty of Medicine, Iran University of Medical Science, Tehran, Iran 5 Radiation Biology Research Center, Iran University of Medical Science, Tehran, Iran
5- Department of Radiation Oncology, Omid Hospital, Esfahan University of Medical Sciences, Isfahan, Iran |
|
|
Abstract: (271 Views) |
Background: Improving segmentation accuracy in CT images of patients with breast cancer plays a crucial role in designing effective treatment plans. This study aimed to evaluate the performance of two deep neural network models, including a U-Net architecture and a combined U-Net with ResNet-18, in this domain.
Materials and methods: Data from 120 patients, comprising 6,890 CT slices, were annotated by expert oncologists and used as the reference standard. These annotations included the heart, left lung, planning target volume (PTV), gross tumor volume (GTV), background, and skin. After data preprocessing, hyperparameters were optimized using a random search method. The models were trained and evaluated based on various performance metrics. Results indicated that the combined model outperformed the U-Net alone in identifying tumor volumes and sensitive structures.
Results: The Dice score reached to 60.35% for GTV and 76.94% for PTV, whereas the U-Net model achieved 39.22% and 39.47%, respectively. These findings demonstrated that the integrated architecture provided significant improvements in classification and detection accuracy, affirmed that combining these architectures enhanced segmentation performance.
Conclusion: Utilizing this approach can significantly improve treatment planning processes and reduce targeting errors. The results clearly show that the combined U-Net and ResNet-18 architecture performs better than the U-Net alone. However, further validation with larger datasets is necessary to establish its clinical applicability.
|
|
| Keywords: Breast cancer, Radiotherapy, Inverse treatment planning, Deep neural networks, U-Net, Resnet18. |
|
|
Full-Text [PDF 853 kb]
(152 Downloads)
|
Semi-pilot: Controlled/Randomized clinical trial |
Subject:
Medical Physics
Received: 2025/09/22 | Accepted: 2026/01/2 | Published: 2026/09/1
|
|
|
|
|
|
|
| References |
1. Shaaban SM, Nawaz M, Said Y, Barr M. An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques. Engineering, Technology & Applied Science Research 2023;13:12415-22. [ DOI:10.48084/etasr.6518] 2. Yerramilli D, Xu AJ, Gillespie EF, Shepherd AF, Beal K, Gomez D, et al. Palliative Radiation Therapy for Oncologic Emergencies in the Setting of COVID-19: Approaches to Balancing Risks and Benefits. Adv Radiat Oncol 2020;5:589-94. [ DOI:10.1016/j.adro.2020.04.001] 3. Trapani D, Ginsburg O, Fadelu T, Lin NU, Hassett M, Ilbawi AM, et al. Global challenges and policy solutions in breast cancer control. Cancer Treat Rev 2022;104:102339. [ DOI:10.1016/j.ctrv.2022.102339] 4. Van de Steene J, Linthout N, de Mey J, Vinh-Hung V, Claassens C, Noppen M, et al. Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol 2002;62:37-49. [ DOI:10.1016/S0167-8140(01)00453-4] 5. Chung Y, Kim JW, Shin KH, Kim SS, Ahn SJ, Park W, et al. Dummy Run of Quality Assurance Program in a Phase 3 Randomized Trial Investigating the Role of Internal Mammary Lymph Node Irradiation in Breast Cancer Patients: Korean Radiation Oncology Group 08-06 Study. Int J Radiat Oncol Biol Phys 2015;91:419-26. [ DOI:10.1016/j.ijrobp.2014.10.022] 6. Khodadadi Shoushtari F, Sina S, Dehkordi ANV. Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights. Phys Med 2022;100:51-63. [ DOI:10.1016/j.ejmp.2022.06.007] 7. Ciardo D, Argenone A, Boboc GI, Cucciarelli F, De Rose F, De Santis MC, et al. Variability in axillary lymph node delineation for breast cancer radiotherapy in presence of guidelines on a multi-institutional platform. Acta Oncol 2017;56:1081-88. [ DOI:10.1080/0284186X.2017.1325004] 8. Yu Y, Wang C, Fu Q, Kou R, Huang F, Yang B, et al. Techniques and Challenges of Image Segmentation: A Review. Electronics. 2023;12:1199. [ DOI:10.3390/electronics12051199] 9. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44. [ DOI:10.1038/nature14539] 10. Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw 2015;61:85-117. [ DOI:10.1016/j.neunet.2014.09.003] 11. Shamshirband S, Fathi M, Dehzangi A, Chronopoulos AT, Alinejad-Rokny H. A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. J Biomed Inform 2021;113:103627. [ DOI:10.1016/j.jbi.2020.103627] 12. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018;19:1236-46. [ DOI:10.1093/bib/bbx044] 13. Ling DC, Moppins BL, Champ CE, Gorantla VC, Beriwal S. Quality of Regional Nodal Irradiation Plans in Breast Cancer Patients Across a Large Network-Can We Translate Results From Randomized Trials Into the Clinic? Pract Radiat Oncol 2021;11:e30-e35. [ DOI:10.1016/j.prro.2020.06.007] 14. Dehkordi AN, Kamali-Asl A, Ewing JR, Wen N, Chetty IJ, Bagher-Ebadian H. An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies. NMR Biomed 2017;30. [ DOI:10.1002/nbm.3682] 15. Chung SY, Chang JS, Choi MS, Chang Y, Choi BS, Chun J, et al. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiat Oncol 2021;16:44. [ DOI:10.1186/s13014-021-01771-z] 16. Zhang J, Yang J, An T, Wu P, Ma C, Zhang C, et al. AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding. Journal of the ASABE 2024;67:493-500. [ DOI:10.13031/ja.15682] 17. Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017;39:640651. [ DOI:10.1109/TPAMI.2016.2572683] 18. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. 234-241). Cham: Springer international publishing. [ DOI:10.1007/978-3-319-24574-4_28] 19. Caballo M, Pangallo DR, Mann RM, Sechopoulos I. Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence. Comput Biol Med 2020;118:103629. [ DOI:10.1016/j.compbiomed.2020.103629] 20. Colbert ZM, Ramachandran P. Auto-segmentation of thoracic organs in CT scans of breast cancer patients using a 3D U-net cascaded into 2D patchGANs. Biomed Phys Eng Express 2023;9. [ DOI:10.1088/2057-1976/ace631] 21. Jiang X, Zhang R, Nie S. Image Segmentation Based on Level Set Method. Physics Procedia 2012;33:840-84. [ DOI:10.1016/j.phpro.2012.05.143] 22. He K, Zhang X, Ren S, Sun J. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-78. [ DOI:10.1109/CVPR.2016.90] 23. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014 Dec 22. 24. Khodadadi Shoushtari F, Dehkordi ANV, Sina S. Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM. Ann Biomed Eng 2024;52:1359-77. [ DOI:10.1007/s10439-024-03461-9] 25. Kazemimoghadam M, Yang Z, Chen M, Rahimi A, Kim N, Alluri P, et al. A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI). Phys Med Biol 2023;68:10.1088/1361-6560/accf5e. [ DOI:10.1088/1361-6560/accf5e] |
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
|
Ahmadi H, NV Dehkordi A, Azimifar F, Mahdavi S R M, Roayaei M. Enhancing medical image segmentation accuracy using a hybrid U-Net and ResNet18 architecture for improved treatment planning. MEDICAL SCIENCES 2026; 36 (3) :289-301 URL: http://tmuj.iautmu.ac.ir/article-1-2433-en.html
|
