Accurate　automatic　segmentation　of　medical　images　is　an　important　basis　for　clinical　imaging　diagnosis　and　3D　image　reconstruction.　However,　medical　image　data　has　small　contrast　differences　between　target　objects,　is　greatly　affected　by　organ　movement,　and　the　scale　of　labeled　samples　is　small.　Therefore,　it　is　still　a　difficult　problem　to　establish　a　high-performance　medical　segmentation　model　under　few　samples.　In　view　of　the　poor　performance　of　the　mainstream　prototype　learning　few-shot　segmentation　network　for　medical　image　boundary　segmentation,　an　iterative　boundary　refinement　based　few-shot　segmentation　network　(IBR-FSS-Net)　is　proposed.　Based　on　the　few-shot　segmentation　framework　of　dual-branch　prototype　learning,　the　category　attention　mechanism　and　dense　comparison　module　(DCM)　are　introduced　to　iteratively　refine　the　coarse　segmentation　mask,　and　guide　the　segmentation　model　to　focus　on　the　boundary　during　multiple　iterative　learning　processes,　thereby　improving　the　boundary　segmentation　accuracy.　In　order　to　further　overcome　the　problem　of　few　training　samples　and　insufficient　diversity　of　medical　images,　this　paper　uses　the　super-pixel　method　to　generate　pseudo-labels　and　expand　the　training　data　to　improve　the　generalization　of　the　model.　Experiments　on　the　mainstream　ABD-MR　and　ABD-CT　medical　image　segmentation　public　datasets　are　done,　we　conduct　extensive　comparative　analysis　and　ablation　experiments　with　various　existing　advanced　medical　image　few-shot　segmentation　methods.　The　results　show　that　our　method　effectively　improves　the　segmentation　performance　of　unseen　medical　categories.　©　2024　Science　Press.　All　rights　reserved.