Purpose:　Due　to　the　different　posture　of　the　subject　and　settings　of　CT　scanners,　the　CT　images　of　the　human　temporal　bone　should　be　geometrically　aligned　with　multiplanar　reconstruction　to　ensure　the　symmetry　of　the　bilateral　anatomical　structure.　Manual　alignment　is　a　time-consuming　task　for　radiologists　and　an　important　preprocessing　step　for　further　computer-aided　CT　analysis.　We　propose　a　fully　automatic　alignment　algorithm　for　temporal　bone　CT　images　via　lateral　semicircular　canals　(LSCs) segmentation.　Methods:　The　LSCs　are　segmented　with　our　proposed　multifeature　fusion　network　as　anchors　at　first.　Then,　we　define　a　standard　3D　coordinate　system　and　propose　an　alignment procedure.　Results:　The　experimental　results　show　that　our　LSC　segmentation　network　achieved　a　higher　segmentation　accuracy.　The　acceptable　rate　is　achieved　85%　over　910　raw　temporal　bone　CT　sequences.　The　alignment　speed　is　reduced　from　10　min　by　manual　to 60s.　Conclusions:　Aiming　at　the　problem　of　bilateral　asymmetry　in　the　raw　temporal　bone　CT　images,　we　propose　an　automatic　geometric　alignment　method.　Our　proposed　method　can　help　to　perform　alignment　of　temporal　bone　CT　images efficiently.　©　2022　American　Association　of　Physicists　in　Medicine.