USING THE ADAPTIVERANDOARMASKAUGMENTATION FUNCTION FOR MEDICAL IMAGE AUGMENTATION AND MASK INPAINTING WITH DEEP LEARNING

Zainab Adnan  Jwad; Israa Hadi  Ali

doi:10.30572/2018/KJE/170115

Authors

Zainab Adnan Jwad PhD student, Department of Software, College of Information Technology, University of Babylon, Babylon, Iraq
Dr. israa hadi Professor, Dr., Department of Software, College of Information Technology, University of Babylon, Babylon, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170115

Keywords:

Data augmentation, Deep learning, Medical Image, Dice score, Elastic deformation, Morphological operation, Noise injection

Abstract

The segmentation of medical images via deep learning is troubled by two primary issues: lack of segmented data and difficulty of shapes. Modern approaches for data augmentation have clearly surpassed classical techniques like shifting, rotating and flipping in terms of performance. The AdaptiveRandOARMaskAugmentation method implements elastic deformations alongside dynamic deformations and intelligent edge noise injection to develop authentic anatomical changes in training data. This method has been deployed within the MONAI framework because it enables interoperability for deep learning systems. The approach outperformed traditional approaches by obtaining a 7.4% Dice score enhancement alongside a 2.9 mm reduction of HD95 on OpenKBP radiological planning dataset tests. The method retained its 12% accuracy decrease when elastic deformation was severed from testing procedures. The research shows the promising practical value of this approach for radiological planning since it boosts reliable critical organ recognition performance across anatomically comparable areas. The technique solves both incomplete data problems and class distribution problems to improve model outcomes for real-world applications

Downloads

Download data is not yet available.

Author Biography

Dr. israa hadi, Professor, Dr., Department of Software, College of Information Technology, University of Babylon, Babylon, Iraq

Professor, Ph.D. Department of Software, College of Information Technology, University of Babylon, Babylon, Iraq [email protected]

References

Alghazaly, S.M. (2025) ‘Danger Indicators of Natural Radionuclides in Consumed Products in the City of Hilla Markets’, Iraqi Journal of Science, 66(5), pp. 1914–1924. Available at: https://doi.org/10.24996/ijs.2025.66.5.11. DOI: https://doi.org/10.24996/ijs.2025.66.5.11

As, A. (1998) ‘Elastic Model-Based Segmentation of 2-D and 3-D Neuroradiological Data Sets’, 18(10), pp. 828–839. DOI: https://doi.org/10.1109/42.811260

Castro, E., Cardoso, J.S. and Pereira, J.C. (2018) ‘Elastic deformations for data augmentation in breast cancer mass detection’, in 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, pp. 230–234. Available at: https://doi.org/10.1109/BHI.2018.8333411. DOI: https://doi.org/10.1109/BHI.2018.8333411

Chalcroft, L. et al. (2024) ‘Synthetic Data for Robust Stroke Segmentation’, pp. 1–17. Available at: http://arxiv.org/abs/2404.01946.

Chandola, Y. et al. (2024) ‘Data Augmentation Techniques applied to Medical Images’, International Journal of Research Publication and Reviews Journal homepage: www.ijrpr.com, 5(7), pp. 483–501. Available at: www.ijrpr.com.

Fadel, N., Abbood, I.K. and Gheni, H.Q. (2022) ‘INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Best Classification of Continuous Data Based on Hybrid Decision Tree’, 10, pp. 388–392.

Hasan, A. and Mazinani, M. (2025) ‘DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING’, Kufa Journal of Engineering, 16(1), pp. 463–478. Available at: https://doi.org/10.30572/2018/KJE/160125. DOI: https://doi.org/10.30572/2018/KJE/160125

Jungo, A. et al. (2021) ‘pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis’, Computer Methods and Programs in Biomedicine, 198, p. 105796. Available at: https://doi.org/10.1016/j.cmpb.2020.105796. DOI: https://doi.org/10.1016/j.cmpb.2020.105796

Jwad, Z.A. and Ali, I.H. (2025) ‘Transformer Decoder-enhanced Swin UNETR for Multi-organ Semantic Segmentation on OpenKBP: Improving Radiotherapy Planning Accuracy’, Karbala International Journal of Modern Science, 11(2), pp. 340–352. Available at: https://doi.org/10.33640/2405-609X.3407. DOI: https://doi.org/10.33640/2405-609X.3407

Kamnitsas, K. et al. (2018) ‘Ensembles of multiple models and architectures for robust brain tumour segmentation’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10670 LNCS(Midl), pp. 450–462. Available at: https://doi.org/10.1007/978-3-319-75238-9_38. DOI: https://doi.org/10.1007/978-3-319-75238-9_38

Legrand, F. et al. (2024) ‘Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI’, Algorithms, 17(1), pp. 1–20. Available at: https://doi.org/10.3390/a17010010. DOI: https://doi.org/10.3390/a17010010

Mohammed, H.A., Kareem, S.W. and Mohammed, A.S. (2022) ‘A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION’, Kufa Journal of Engineering, 13(4), pp. 53–69. Available at: https://doi.org/10.30572/2018/KJE/130405. DOI: https://doi.org/10.30572/2018/KJE/130405

Sultan, N.H. (2016) ‘HYBRID IMAGE DENOISING USING WIENER FILTER WITH DISCRETE WAVELET TRANSFORM AND FRAMELET TRANSFORM’, Kufa Journal of Engineering, 7(2), pp. 122–133. Available at: https://doi.org/10.30572/2018/KJE/721211. DOI: https://doi.org/10.30572/2018/KJE/721211

Zhang, Z. et al. (2023) ‘A Novel Noise Injection-based Training Scheme for Better Model Robustness’, (49). Available at: http://arxiv.org/abs/2302.10802.

Zhao, A. et al. (2019) ‘Data augmentation using learned transformations for one-shot medical image segmentation’, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 8535–8545. Available at: https://doi.org/10.1109/CVPR.2019.00874. DOI: https://doi.org/10.1109/CVPR.2019.00874