Background　and　Objective:　:　Automatic　skin　lesion　segmentation　plays　an　important　role　in　computer　-aided　diagnosis　of　skin　diseases.　However,　current　segmentation　networks　cannot　accurately　detect　the　boundaries　of　the　skin　lesion　areas.Methods:　:　In　this　paper,　a　boundary　learning　assisted　network　for　skin　lesion　segmentation　is　proposed,　namely　BLA-Net,　which　adopts　ResNet34　as　backbone　network　under　an　encoder-decoder　framework.　The　overall　architecture　is　divided　into　two　key　components:　Primary　Segmentation　Network　(PSNet)　and　Auxiliary　Boundary　Learning　Network　(ABLNet).　PSNet　is　to　locate　the　skin　lesion　areas.　Dynamic　Deformable　Convolution　is　introduced　into　the　lower　layer　of　the　encoder,　so　that　the　network　can　ef-fectively　deal　with　complex　skin　lesion　objects.　And　a　Global　Context　Information　Extraction　Module　is　proposed　and　embedded　into　the　high　layer　of　the　encoder　to　capture　multi-receptive　field　and　multi　-scale　global　context　features.　ABLNet　is　to　finely　detect　the　boundaries　of　skin　lesion　area　based　on　the　low-level　features　of　the　encoder,　in　which　an　object　regional　attention　mechanism　is　proposed　to　en-hance　the　features　of　lesion　object　area　and　suppress　those　of　irrelevant　regions.　ABLNet　can　assist　the　PSNet　to　realize　accurate　skin　lesion　segmentation.Results:　:　We　verified　the　segmentation　performance　of　the　proposed　method　on　the　two　public　der-moscopy　datasets,　namely　ISBI　2016　and　ISIC　2018.　The　experimental　results　show　that　our　proposed　method　can　achieve　the　Jaccard　Index　of　86.6%,　84.8%　and　the　Dice　Coefficient　of　92.4%,　91.2%　on　ISBI　2016　and　ISIC　2018　datasets,　respectively.Conclusions:　:　Compared　with　existing　methods,　the　proposed　method　can　achieve　the　state-of-the-arts　segmentation　accuracy　with　less　model　parameters,　which　can　assist　dermatologists　in　clinical　diagnosis　and　treatment.(c)　2022　Published　by　Elsevier　B.V.