郑州大学学报（理学版）2026，Vol.58Issue(2)：10-16,7.DOI:10.13705/j.issn.1671-6841.2024125
一种原型优化和细化分割的小样本医学图像分割网络
A Prototype Optimization and Refinement Segmentation Network for Few-shot Medical Image Segmentation
摘要
Abstract
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.关键词
医学图像分割/小样本学习/原型优化/细化分割/注意力机制Key words
medical image segmentation/few-shot learning/prototype optimization/refinement segmen-tation/attention mechanism分类
信息技术与安全科学引用本文复制引用
魏明军,贺海鹏,陈伟彬,刘亚志,李辉..一种原型优化和细化分割的小样本医学图像分割网络[J].郑州大学学报（理学版）,2026,58(2):10-16,7.基金项目
河北省高等学校科学技术研究项目(ZD2022102) （ZD2022102）
