Hydronephrosis　is　a　common　renal　disease　in　children　which　can　lead　to　a　series　of　complications,　and　ultrasonography　is　a　basic　examination　usually　performed　on　suspected　hydronephrosis　patients.　If　we　can　use　deep　learning　approaches　to　judge　and　grade　the　disease　in　the　ultrasonic　examination　stage,　we　can　save　a　lot　of　manpower,　medical　resources,　money,　and　help　the　suffered　patients.　For　the　semantic　segmentation　of　kidney　ultrasound　image,　we　designed　an　Attention-based　Pyramid　Scene　Parsing　Network　(A-PSPNet),　the　core　of　which　is　the　basic　feature　extraction　network　combining　Convolutional　Block　Attention　Module　(CBAM)　and　pyramid　analysis　module.　Experiments　were　carried　out　on　a　hydronephrosis　dataset　containing　1850　annotated　ultrasound　images,　including　the　arrangement　of　attention　units,　statistical　computing　power,　and　comparison　of　the　effectiveness　between　the　benchmark　and　our　proposed　method.　Our　constructed　model　achieved　better　segmentation　performance　than　benchmarks　with　only　little　extra　overhead,　which　validated　the　lightweight　and　effectiveness　of　the　model.