Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models
<|reference_start|>Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models: Purpose: Bone metastasis have a major impact on the quality of life of patients and they are diverse in terms of size and location, making their segmentation complex. Manual segmentation is time-consuming, and expert segmentations are subject to operator variability, which makes obtaining accurate and reproducible segmentations of bone metastasis on CT-scans a challenging yet important task to achieve. Materials and Methods: Deep learning methods tackle segmentation tasks efficiently but require large datasets along with expert manual segmentations to generalize on new images. We propose an automated data synthesis pipeline using 3D Denoising Diffusion Probabilistic Models (DDPM) to enchance the segmentation of femoral metastasis from CT-scan volumes of patients. We used 29 existing lesions along with 26 healthy femurs to create new realistic synthetic metastatic images, and trained a DDPM to improve the diversity and realism of the simulated volumes. We also investigated the operator variability on manual segmentation. Results: We created 5675 new volumes, then trained 3D U-Net segmentation models on real and synthetic data to compare segmentation performance, and we evaluated the performance of the models depending on the amount of synthetic data used in training. Conclusion: Our results showed that segmentation models trained with synthetic data outperformed those trained on real volumes only, and that those models perform especially well when considering operator variability.<|reference_end|>
@article{saillard2024enhanced,
    title={Enhanced segmentation of femoral bone metastasis in CT scans of patients
  using synthetic data generation with 3D diffusion models},
    author={Emile Saillard, Aur'elie Levillain, David Mitton, Jean-Baptiste
  Pialat, Cyrille Confavreux, H'el`ene Follet, and Thomas Grenier},
    journal={arXiv preprint arXiv:2409.11011},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11011},
    primaryClass={eess.IV cs.AI cs.CV}
}