Background:　With　the　rapid　advancement　of　medical　imaging　technology,　the　demand　for　accurate　segmentation　of　medical　images　is　increasing.　However,　most　existing　methods　are　unable　to　capture　locality　and　long-range　dependency　information　in　integrated　ways　for　medical　images.　Method:　In　this　paper,　we　propose　an　elegant　segmentation　framework　for　medical　images　named　TC-Net,　which　can　utilize　both　the　locality-aware　and　long-range　dependencies　in　the　medical　images.　As　for　the　locality-aware　perspective,　we　employ　a　CNN-based　encoder　and　decoder　structure.　The　CNN　branch　uses　the　locality　of　convolution　operations　to　dig　out　local　information　in　medical　images.　As　for　the　long-range　dependencies,　we　construct　a　Transformer　branch　to　focus　on　the　global　context.　Additionally,　we　proposed　a　locality-aware　and　long-range　dependency　concatenation　strategy　(LLCS)　to　aggregate　the　feature　maps　obtained　from　the　two　subbranches.　Finally,　we　present　a　dynamic　cyclical　focal　loss　(DCFL)　to　address　the　class　imbalance　problem　in　multi-lesion　segmentation.　Results:　Comprehensive　experiments　were　conducted　on　lesion　segmentation　tasks　using　two　fundus　image　databases　and　a　skin　image　database.　The　TC-Net　achieves　scores　of　0.6985　and　0.5171　in　the　metric　of　mean　pixel　accuracy　on　the　IDRiD　and　DDR　databases,　respectively.　Moreover,　on　the　skin　image　database,　the　TC-Net　reached　mean　pixel　accuracy　of　0.8886.　The　experiment　results　demonstrate　that　the　proposed　method　achieves　better　performance　than　other　deep　learning　segmentation　schemes.　Furthermore,　the　proposed　DCFL　achieves　higher　performance　than　other　loss　functions　in　multi-lesion　segmentation.　Significance:　The　proposed　TC-Net　is　a　promising　new　framework　for　multi-lesion　medical　image　segmentation　and　many　other　challenging　image　segmentation　tasks.　©　2001　Elsevier　Science.　All　rights　reserved.　©　2023　Elsevier　Ltd