Hydronephrosis　may　lead　to　many　potential　diseases,　and　the　diagnosis　of　hydronephrosis　is　time-consuming　and　laborious.　To　assist　physicians　in　hydronephrosis　diagnosis　and　treatment　planning,　an　accurate　and　automatic　kidney　segmentation　method　is　highly　required　in　clinical　practice.　In　recent　years,　deep　convolutional　neural　networks　such　as　Unet　plays　a　key　role　in　the　field　of　image　segmentation,　but　Unet　itself　cannot　adjust　the　receptive　field　actively,　which　may　result　in　poor　attention　to　the　characteristics　of　the　segmented　target.　We　propose　an　encoder-decoder　network　with　weighted　skip　connections　and　the　idea　of　hierarchical　equal　resolution　that　can　manually　control　the　receptive　field.　We　evaluated　our　method　by　comparing　it　with　various　classical　networks　using　a　dataset　of　1850　annotated　images.　The　MPA　of　the　model　is　94.12　and　the　MIoU　is　89.49,　which　outperformed　other　classical　networks　we　compared　to.