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Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation

arXiv 2506.12106 ↗First posted 13 Jun 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

AI requires extensive datasets, while medical data is subject to high data protection. Anonymization is essential, but poses a challenge for some regions, such as the head, as identifying structures overlap with regions of clinical interest. Synthetic data offers a potential solution, but studies often lack rigorous evaluation of realism and utility. Therefore, we investigate to what extent synthetic data can replace real data in segmentation tasks. We employed head and neck cancer CT scans and brain glioma MRI scans from two large datasets. Synthetic data were generated using generative adversarial networks and diffusion models. We evaluated the quality of the synthetic data using MAE, MS-SSIM, Radiomics and a Visual Turing Test (VTT) performed by 5 radiologists and their usefulness in segmentation tasks using DSC. Radiomics indicates high fidelity of synthetic MRIs, but fall short in producing highly realistic CT tissue, with correlation coefficient of 0.8784 and 0.5461 for MRI and CT tumors, respectively. DSC results indicate limited utility of synthetic data: tumor segmentation achieved DSC=0.064 on CT and 0.834 on MRI, while bone segmentation a mean DSC=0.841. Relation between DSC and correlation is observed, but is limited by the complexity of the task. VTT results show synthetic CTs' utility, but with limited educational applications. Synthetic data can be used independently for the segmentation task, although limited by the complexity of the structures to segment. Advancing generative models to better tolerate heterogeneous inputs and learn subtle details is essential for enhancing their realism and expanding their application potential.

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References · 16

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  8. 2017Radiomics strategies for risk assessment of tumour failure in head-and-neck cancercited by 31
  9. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 28
  10. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
  11. 2024cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesiscited by 19
  12. 2023Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Imagescited by 16
4 more
  1. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
  2. 2013GBM Volumetry using the 3D Slicer Medical Image Computing Platformcited by 10
  3. 2021Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Studycited by 3
  4. 2024Data Augmentation via Diffusion Model to Enhance AI Fairnesscited by 2

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  2. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  3. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  4. 2024Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs
  5. 2024MAISI: Medical AI for Synthetic Imaging
  6. 2024Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
  7. 2024Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models
  8. 2022Brain Imaging Generation with Latent Diffusion Models