Hand　gesture　segmentation　is　an　initial　and　essential　step　to　classify　hand　gestures,　which　provides　a　simple,　intuitive,　concise　and　natural　way　for　human-computer　interaction,　human-robot　interaction.　However,　hand　gestures　segmentation　with　various　hand　shapes　cluttered　background　is　still　a　challenging　problem.　To　solve　the　problem,　a　Multi-Branch　Cascade　Transformer　Network　(MBCT-Net)　is　proposed　to　segment　hand　regions　from　the　cluttered　background　based　on　encoder-decoder　convolutional　neural　networks,　the　encoder　of　the　MBCT-Net　consists　of　a　deep　convolutional　neural　network　(DCNN)　module　and　a　multi-branch　cascade　Transformer　(MBCT)　module.　Furthermore,　the　MBCT　module　is　designed　to　represent　local　details　and　global　semantic　information　of　hand　gestures.　Moreover,　to　enhance　semantical　interaction　between　different　windows　and　expand　the　receptive　fields　of　MBCT-Net,　we　design　a　multi-window　self-attention　(MWSA)　block　in　each　branch　of　MBCT　module　to　extract　features　of　hand　gestures.　The　MWSA　block　not　only　reduces　the　amount　of　calculation,　but　also　enhances　semantic　interactions　between　different　windows.　To　verify　effectiveness　of　the　proposed　MBCT-Net,　corresponding　experiments　have　been　conducted,　and　the　experimental　results　prove　correctness　of　the　MBCT-Net.