The　segmentation　and　reconstruction　of　the　aortic　vessel　tree　(AVT)　is　necessary　in　detecting　aortic　diseases.　Currently,　the　mainstream　method　must　be　deployed　manually,　which　is　time-consuming　and　requires　an　experienced　radiologist/physician.　Automatic　segmentation　methods　developed　in　recent　years　have　performed　well　on　single-centered　datasets.　However,　their　performance　degraded　on　multi-centered　datasets　due　to　the　various　specifications　of　the　data.　We　propose　a　3D　U-Net-based　robust　aortic　segmentation　framework　to　address　the　problem.　We　implied　Hounsfield　Units　(HU)　adaptive　method　during　preprocessing　to　reduce　the　variety　of　intensity　distribution　of　the　intercenter　images.　We　insert　convolutional　block　attention　modules　(CBAM)　in　our　network　to　improve　its　channel　and　spatial　representation　ability.　Furthermore,　we　set　a　two-stage　training　process　and　introduce　the　Hausdorff　distance　(HD)　loss　in　the　second　stage　to　optimize　the　structure　of　the　segmentation　results.　Using　a　specific　validation　set　collected　from　the　multicenter　AVT　dataset　which　includes　samples　D5,　D6,　K4,　K5,　R5,　R6,　our　proposed　method　reached　an　average　Dice　Similarity　Coefficient　(DSC)　of　0.9396　and　an　average　HD　of　16.1.