The　disadvantage　of　tongue　image　segmentation　in　traditional　Chinese　medicine　are　low　accuracy,　slow　segmentation　speed　and　manual　calibration　of　candidate　regions.To　solve　these　problems,　we　propose　an　end-to-end　tongue　image　segmentation　algorithm.　Compared　with　the　traditional　tongue　segmentation　algorithm,　more　accurate　segmentation　results　can　be　obtained　by　the　proposed　method　which　does　not　need　any　manual　operation.　Firstly,　the　atrous　convolution　algorithm　is　used　to　increase　the　feature　map　of　the　network　without　increasing　the　parameters.　Secondly,　the　atrous　spatial　pyramid　pooling　(ASPP)　module　is　used　to　enable　the　network　to　learn　the　multi-scale　feature　of　the　tongue　image　through　different　receptive　fields.　Finally,　the　deep　convolutional　neural　networks　(DCNN)　are　combined　with　fully　connected　conditional　random　fields　(CRF)　to　refine　the　edge　of　the　segmented　tongue　image.　The　experimental　results　show　that　the　proposed　method　outperforms　traditional　tongue　image　segmentation　algorithm　and　popular　DCNN　with　higher　segmentation　accuracy,　and　the　mean　intersection　over　union　reaches　95.41%.　©　2019,　Editorial　Board　of　JBUAA.　All　right　reserved.