Computed　Tomography　(CT)　has　become　an　important　way　for　examining　the　critical　anatomical　organs　of　the　human　temporal　bone　in　the　diagnosis　and　treatment　of　ear　diseases.　Segmentation　of　the　critical　anatomical　organs　is　an　important　fundamental　step　for　the　computer　assistant　analysis　of　human　temporal　bone　CT　images.　However,　it　is　challenging　to　segment　sophisticated　and　small　organs.　To　deal　with　this　issue,　a　novel　3D　Deep　Supervised　Densely　Network　(3D-DSD　Net)　is　proposed　in　this　paper.　The　network　adopts　a　dense　connection　design　and　a　3D　multi-pooling　feature　fusion　strategy　in　the　encoding　stage　of　the　3D-Unet,　and　a　3D　deep　supervised　mechanism　is　employed　in　the　decoding　stage.　The　experimental　results　show　that　our　method　achieved　competitive　performance　in　the　CT　data　segmentation　task　of　the　small　organs　in　the　temporal　bone.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.