This　paper　presents　a　two-step　method　to　automatically　and　accurately　segment　the　dental　crown　components　from　CT　images.　Firstly,　a　multi-scale　attention　based　U-Net　model　is　proposed　for　pulp　segmentation,　which　is　embedded　with　global　and　local　attention　modules.　The　constructed　attention　modules　can　automatically　aggregate　pixel-wise　contextual　information　and　focus　on　catching　the　real　dental　pulp　region.　Secondly,　two　efficient　level　set　models　are　proposed:　one　is　the　shape　constraint-based　level　set　model　for　enamel　and　dentin　segmentation,　the　other　is　the　region　mutual　exclusion-based　level　set　model　for　neighboring　teeth　segmentation.　The　proposed　shape　constraint　term　can　better　handle　topology　changes　of　teeth　and　the　region　mutual　exclusion　term　can　more　effectively　avoid　intersecting　segmentation.　Besides,　a　starting　slice　initialization　method　is　introduced　to　achieve　automatic　segmentation,　and　an　accurate　contour　propagation　strategy　is　developed　for　slice-by-slice　segmentation.　We　set　up　a　series　of　comparative　experiments　for　evaluation.　Experimental　results　verify　that　the　proposed　method　obtains　promising　performance　for　each　crown　component　segmentation,　and　outperforms　state-of-the-art　tooth　segmentation　methods　in　terms　of　accuracy.　This　suggests　that　the　proposed　method　can　be　used　to　accurately　segment　the　crown　components　for　precise　tooth　preparation　treatment.　Note　to　Practitioners—The　motivation　of　this　work　is　to　reduce　the　burden　on　dentists　during　tooth　preparation　treatment,　which　requires　accurate　segmentation　of　crown　components　(i.e.,　enamel,　dentin,　and　pulp)　from　dental　CT　images.　Existing　methods　only　focused　on　the　segmentation　of　teeth　or　alveolar　bone.　Therefore,　we　present　a　novel　automatic　segmentation　model　for　the　dental　crown　components　with　high　accuracy.　A　key　strength　of　this　study　is　the　combination　of　a　data-driven　method　(deep　learning)　and　model-driven　methods　(level-set),　which　can　provide　good　accuracy　under　limited　training　samples.　This　ability　is　highly　desirable　for　practitioners　by　saving　labor-intensive,　costly　labeling　efforts.　Furthermore,　our　proposed　method　will　provide　tools　to　help　reduce　subjectivity　and　human　errors,　as　well　as　streamline　and　expedite　the　clinical　workflow.　This　will　significantly　facilitate　tooth　preparation　automation.　IEEE