Accurate　vestibule　segmentation　for　CT　images　is　of　great　significance　for　the　clinical　diagnosis　of　congenital　ear　malformations　and　cochlear　implant.　However,　it　is　still　a　challenging　task　due　to　extremely　small　size　and　irregular　shape　of　vestibule.　Here,　a　vestibule　segmentation　network　for　CT　images　is　proposed　under　the　basic　encoder-decoder　framework.　Firstly,　a　residual　block　based　on　channel　attention　mechanism,　named　Res-CA　block,　is　designed　to　guide　the　network　to　enhance　the　important　features　for　the　segmentation　tasks　while　suppressing　the　irrelevant　ones.　And　then,　a　global　context-aware　pyramid　feature　extraction　(GCPFE)　module　is　proposed　to　capture　multi-receptive-field　global　context　information.　Finally,　active　contour　with　elastic　(ACE)　loss　function　is　adopted　to　guide　network　learning　more　detailed　information　of　the　boundary.　Furthermore,　deep　supervision　(DS)　mechanism　is　employed　to　locate　the　boundaries　finely,　improving　the　robustness　of　the　network.　The　experiments　are　conducted　on　the　self-established　VestibuleDataset　and　UHRCT-Dataset,　as　well　as　publicly　available　retinal　dataset,　namely　DRIVE,　to　comprehensively　verify　the　robustness　and　generalization　capability　of　the　proposed　segmentation　network.　The　experimental　results　show　that　the　proposed　network　can　achieve　a　superior　performance.