Image　segmentation　methods　based　on　deep　learning　can　help　doctors　segment　the　region　of　interest　(ROI)　in　medical　images　rapidly.　Hypopharyngeal　cancer　(HPC)　is　a　rare　cancer,　so　there　are　fewer　Magnetic　Resonance　Images　(MRIs)　for　HPC.　MRIs　of　HPC　often　have　problems　of　uneven　brightness.　Therefore,　it　is　a　major　challenge　to　use　deep　learning　to　build　a　semantic　segmentation　network　for　HPC.　To　solve　the　problem,　we　choose　ResNetl8　(pretrained　on　ImageNet　dataset)　as　the　encoder　and　compare　the　effects　of　different　convolution　neural　networks　as　decoders.　Compare　with　other　methods,　i.e.,　U-Net,　DeepLabV3+,　U-Net++,　and　EfficientUNet++,　ResUNet++　achieves　splendid　prediction　performance　and　outperforms　other　methods　on　HPC　datasets,　which　achieves　the　highest　Dice　score　of　77.34%.