The paper presents two distinct strategies for incorporating segmentation information as a condition into the sampling and training processes of diffusion models for generating knee radiographs.
The first method, Conditional Sampling Method (CSM), starts with a perturbed segmentation guide and iteratively denoises it to generate realistic radiographs while preserving the desired shape. The second method, Conditional Training Method (CTM), directly estimates the score function of the conditional distribution by concatenating the segmentation with the perturbed image as the network input during training.
The results show that the CTM outperforms the CSM and a conventional U-Net model in terms of both visual quality and quantitative metrics like mean absolute error and peak signal-to-noise ratio. The CTM can generate radiographs that closely match the fine details of the provided segmentation guides, demonstrating the potential of conditional diffusion models for medical image synthesis tasks.
The authors also discuss future research directions, such as modeling 3D probabilistic distributions with 2D conditional information to enable CT reconstruction from the generated projections, as well as incorporating clinical datasets.
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by Siyuan Mei,F... at arxiv.org 04-05-2024
https://arxiv.org/pdf/2404.03541.pdfDeeper Inquiries