FDiff-Fusion：基于模糊学习的去噪扩散融合网络用于3D医学图像分割
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation
AI总结：
针对医学图像分割中边界不确定、区域模糊导致结果不稳定的问题，提出融合去噪扩散模型与U-Net的FDiff-Fusion，并在BRATS 2020和BTCV上验证其Dice与HD95性能优势。
摘要：
近年来，去噪扩散模型在图像分割建模中取得了显著成功。凭借强大的非线性建模能力和优越的泛化性能，去噪扩散模型逐渐被应用于医学图像分割任务，为该领域带来了新的视角和方法。然而，现有方法忽视了分割边界的不确定性以及区域的模糊性，导致分割结果不稳定且不准确。为解决这一问题，本文提出了一种基于模糊学习的去噪扩散融合网络，用于3D医学图像分割，即FDiff-Fusion。通过将去噪扩散模型集成到经典的U-Net网络中，该模型能够从输入医学图像中有效提取丰富的语义信息，从而为医学图像分割提供出色的像素级表示。……最后，为验证FDiff-Fusion的有效性，我们在BRATS 2020脑肿瘤数据集和BTCV腹部多器官数据集上将其与现有先进分割网络进行了比较。结果表明，FDiff-Fusion在这两个数据集上显著提升了Dice分数和HD95距离，证明了其在医学图像分割任务中的优越性。
英文摘要：
In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. ... Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
