基于扩散模型的自监督预训练血管内超声图像分割方法

Self-Supervised Pre-Training for Intravascular Ultrasound Image Segmentation Method Based on Diffusion Model

为了克服获取大规模有标签数据集的困难,引入一种基于扩散模型的代理任务,通过自监督学习从未标注的数据集中获取先验知识,并在小规模有标签的数据集上进行微调。受扩散模型的启发,使用不同等级的噪声与原图加权混合作为模型输入,并训练模型预测输入的噪声,以更好地学习血管内超声(IVUS)图像的表征。此外,引入均方误差(MSE)和结构相似性指数(SSIM)的联合损失函数以提高模型性能。所提方法在20%数据集上的实验结果表明:所得内膜和中膜的Jaccard度量(JM)系数相比于随机初始化的结果分别提高了0.044和0.101,Hausdorff距离(HD)系数分别改进了0.216和0.107,达到了与使用100%数据集训练相近的结果。该框架可以适用于任何结构图像分割模型,并在确保分割效果的同时,显著降低了对真值标签的依赖。

To overcome the difficulty of obtaining large annotated datasets,a proxy task based on a diffusion model was introduced,allowing for self-supervised learning of a priori knowledge from unlabeled datasets,followed by fine-tuning on a small labeled dataset.Inspired by the diffusion model,different levels of noise are weighted with the original images as inputs to the model.By training the model to predict the input noise,a more robust learning of the representation of intravascular ultrasound(IVUS)images at the pixel level was achieved.Additionally,the combined loss function of mean square error(MSE)and structural similarity index(SSIM)was introduced to improve the performance of the model.The experimental results of this method on 20%dataset demonstrate that the Jaccard metric coefficients of the lumen and meida are increased by 0.044 and 0.101,respectively,compared with result of random initialization,and the Hausdorff distance coefficients are improved by 0.216 and 0.107,respectively,compared with result of random initialization,which is similar to the result of using 100%dataset for training.This framework applies to any structural image segmentation model and significantly reduces the reliance on ground truth while ensuring segmentation effectiveness.