Background　and　Objectives:　Automatic　airway　segmentation　from　chest　computed　tomography　(CT)　scans　plays　an　important　role　in　pulmonary　disease　diagnosis　and　computer-assisted　therapy.　However,　low　contrast　at　peripheral　branches　and　complex　tree-like　structures　remain　as　two　mainly　challenges　for　airway　segmentation.　Recent　research　has　illustrated　that　deep　learning　methods　perform　well　in　segmentation　tasks.　Motivated　by　these　works,　a　coarse-to-fine　segmentation　framework　is　proposed　to　obtain　a　complete　airway　tree.　Methods:　Our　framework　segments　the　overall　airway　and　small　branches　via　the　multi-information　fusion　convolution　neural　network　(Mif-CNN)　and　the　CNN-based　region　growing,　respectively.　In　Mif-CNN,　atrous　spatial　pyramid　pooling　(ASPP)　is　integrated　into　a　u-shaped　network,　and　it　can　expend　the　receptive　field　and　capture　multi-scale　information.　Meanwhile,　boundary　and　location　information　are　incorporated　into　semantic　information.　These　information　are　fused　to　help　Mif-CNN　utilize　additional　context　knowledge　and　useful　features.　To　improve　the　performance　of　the　segmentation　result,　the　CNN-based　region　growing　method　is　designed　to　focus　on　obtaining　small　branches.　A　voxel　classification　network　(VCN),　which　can　entirely　capture　the　rich　information　around　each　voxel,　is　applied　to　classify　the　voxels　into　airway　and　non-airway.　In　addition,　a　shape　reconstruction　method　is　used　to　refine　the　airway　tree.　Results:　We　evaluate　our　method　on　a　private　dataset　and　a　public　dataset　from　EXACT09.　Compared　with　the　segmentation　results　from　other　methods,　our　method　demonstrated　promising　accuracy　in　complete　airway　tree　segmentation.　In　the　private　dataset,　the　Dice　similarity　coefficient　(DSC),　Intersection　over　Union　(IoU),　false　positive　rate　(FPR),　and　sensitivity　are　93.5%,　87.8%,　0.015%,　and　90.8%,　respectively.　In　the　public　dataset,　the　DSC,　IoU,　FPR,　and　sensitivity　are　95.8%,　91.9%,　0.053%　and　96.6%,　respectively.　Conclusion:　The　proposed　Mif-CNN　and　CNN-based　region　growing　method　segment　the　airway　tree　accurately　and　efficiently　in　CT　scans.　Experimental　results　also　demonstrate　that　the　framework　is　ready　for　application　in　computer-aided　diagnosis　systems　for　lung　disease　and　other　related　works.　©　2021