With　the　development　of　deep　learning,　using　convolutional　neural　networks　for　semantic　segmentation　has　received　a　large　amount　of　attention.　Numerous　convolutional　neural　networks　architecture　has　been　proposed.　In　biomedical　image　processing,　U-Net　has　achieved　great　remarkable　achievement.　However,　due　to　the　feeble　convolution　operations　for　extracting　complex　image　information,　the　U-Net　presents　a　poor　performance　in　general　semantic　segmentation.　Therefore,　in　this　paper,　we　propose　a　new　neural　network　framework,　called　＇Deeper　Residual　U-Net＇　for　general　image　semantic　segmentation.　In　our　method,　we　apply　ResNet101　for　extracting　features　and　use　a　double　features　fusion　mechanism　compared　to　U-net.　In　the　first　time,　the　Deeper　Residual　U-Net　up　sample　each　stage　features　and　fuses　them　with　features　of　the　previous　layer　one　by　one,　which　make　low-level　features　contain　more　abstract　information.　In　the　second　time,　it　upsamples　all　fused　features　of　different　stages　to　the　same　size　and　combines　them　to　predict.　We　test　our　network　in　Pascal　VOC　2012　dataset　and　get　mean　accuracy　80.9,　mIoU　accuracy　74.3,　which　already　available　for　general　image　segmentation.　©　2019　Association　for　Computing　Machinery.