Extracting　the　tongue　body　accurately　from　a　digital　tongue　image　is　a　challenge　for　automated　tongue　diagnoses,　as　the　blurred　edge　of　the　tongue　body,　interference　of　pathological　details,　and　the　huge　difference　in　the　size　and　shape　of　the　tongue.　In　this　study,　an　automated　tongue　image　segmentation　method　using　enhanced　fully　convolutional　network　with　encoder-decoder　structure　was　presented.　In　the　frame　of　the　proposed　network,　the　deep　residual　network　was　adopted　as　an　encoder　to　obtain　dense　feature　maps,　and　a　Receptive　Field　Block　was　assembled　behind　the　encoder.　Receptive　Field　Block　can　capture　adequate　global　contextual　prior　because　of　its　structure　of　the　multibranch　convolution　layers　with　varying　kernels.　Moreover,　the　Feature　Pyramid　Network　was　used　as　a　decoder　to　fuse　multiscale　feature　maps　for　gathering　sufficient　positional　information　to　recover　the　clear　contour　of　the　tongue　body.　The　quantitative　evaluation　of　the　segmentation　results　of　300　tongue　images　from　the　SIPL-tongue　dataset　showed　that　the　average　Hausdorff　Distance,　average　Symmetric　Mean　Absolute　Surface　Distance,　average　Dice　Similarity　Coefficient,　average　precision,　average　sensitivity,　and　average　specificity　were　11.2963,　3.4737,　97.26%,　95.66%,　98.97%,　and　98.68%,　respectively.　The　proposed　method　achieved　the　best　performance　compared　with　the　other　four　deep-learning-based　segmentation　methods　(including　SegNet,　FCN,　PSPNet,　and　DeepLab　v3+).　There　were　also　similar　results　on　the　HIT-tongue　dataset.　The　experimental　results　demonstrated　that　the　proposed　method　can　achieve　accurate　tongue　image　segmentation　and　meet　the　practical　requirements　of　automated　tongue　diagnoses.