Deep　learning　approaches　for　tooth　segmentation　employ　convolutional　neural　networks　(CNNs)　or　Transformers　to　derive　tooth　feature　maps　from　extensive　training　datasets.　Tooth　segmentation　serves　as　a　critical　prerequisite　for　clinical　dental　analysis　and　surgical　procedures,　enabling　dentists　to　comprehensively　assess　oral　conditions　and　subsequently　diagnose　pathologies.　Over　the　past　decade,　deep　learning　has　experienced　significant　advancements,　with　researchers　introducing　efficient　models　such　as　U-Net,　Mask　R-CNN,　and　Segmentation　Transformer　(SETR).　Building　upon　these　frameworks,　scholars　have　proposed　numerous　enhancement　and　optimization　modules　to　attain　superior　tooth　segmentation　performance.　This　paper　discusses　the　deep　learning　methods　of　tooth　segmentation　on　dental　panoramic　radiographs　(DPRs),　cone-beam　computed　tomography　(CBCT)　images,　intro　oral　scan　(IOS)　models,　and　others.　Finally,　we　outline　performance-enhancing　techniques　and　suggest　potential　avenues　for　ongoing　research.　Numerous　challenges　remain,　including　data　annotation　and　model　generalization　limitations.　This　paper　offers　insights　for　future　tooth　segmentation　studies,　potentially　facilitating　broader　clinical　adoption.