Ear　computed　tomography　(CT)　has　become　an　important　means　of　diagnosing　ear　diseases,　which　provides　doctors　with　a　chance　of　observing　the　shape　and　components　of　the　key　anatomical　structures　of　the　auditory　system.　Therefore,　it　is　helpful　to　diagnose　the　ear　diseases　early.　However,　the　anatomical　structures　of　the　auditory　system　are　characterized　by　complexity,　sophisticated,　and　large　individual　differences,　meanwhile,　they　are　small　and　difficult　to　segment.　Most　of　the　existing　medical　image　segmentation　algorithms　fail　in　segmenting　the　ear　anatomical　structures.　To　address　the　problem,　a　3D　fully　convolutional　network　(3D-FCN)　based　semantic　segmentation　method　is　proposed　for　the　key　anatomical　structures　of　ear　CT　Images.　We　evaluated　our　approach　on　the　ear　CT　dataset.　Compared　to　the　2D　fully　convolutional　network　(2D-FCN),　the　mean　Dice-Sorensen　Coefficient　(DSC)　of　our　method　is　improved　significantly　in　the　task　of　segmentation　for　six　key　anatomical　structures　of　the　ear.　The　experimental　results　show　that　our　method　can　effectively　improve　the　segmentation　accuracy　of　key　anatomical　structures　of　ear　CT　images.