一种原型优化和细化分割的小样本医学图像分割网络OA

In order to solve the problem of few-shot medical image segmentation with the distribution shift and local edge details between the support set and the query set,a prototype optimization and refinement segmentation network(PORSNet)for few-shot medical image segmentation was proposed.The network contained a prototype loop iteration module,which suppressed the distribution shift between the initial prototype and the query set and enhanced the expressiveness of the prototype by iteratively performing steps such as initial prototype correction,prototype global perception,and prototype distillation.In addi-tion,a prototype refinement segmentation module was included to further process edge detail information through mask-guided aggregation and feature normalization refinement.Extensive experiments on two widely used few-shot medical public datasets,ABD-MRI and ABD-CT,showed that the proposed PORSNet could use a small number of samples to ensure the segmentation effect with good generalization ability.