The　U-Net　and　its　extensions　have　achieved　good　success　in　medical　image　segmentation.　However,　fine-grained　segmentation　of　the　objects　at　their　fuzzy　edges,　which　is　commonly　found　in　medical　images,　is　still　challenging.　In　this　paper,　we　propose　a　U-Net　like　Multi-Stage　Feature　Analysis　Network　(MFA　U-Net)　for　medical　image　segmentation,　which　focus　on　mining　the　reusability　of　the　images　and　features　from　several　perspectives.　Firstly,　a　multi-channel　dimensional　feature　extraction　module　is　proposed,　where　the　input　image　was　reused　by　multiple　branches　of　convolutions　with　different　channels　to　generate　supplement　features　to　the　original　U　shaped　network.　Next,　a　cascaded　U-shaped　network　is　designed　for　deeper　feature　mining　and　analysis,　which　enables　progressive　refinement　of　the　features.　In　the　neck　of　the　cascaded　network,　a　parallel　hybrid　convolution　module　is　designed　that　concatenating　several　types　of　convolutional　methods　to　enhance　the　semantic　representation　ability　of　the　model.　In　short,　by　reusing　of　the　input　images　and　detected　features　in　several　stages,　more　effective　features　were　extracted　and　the　segmentation　performances　were　improved.　The　proposed　algorithm　was　evaluated　by　three　mainstream　2D　color　medical　image　segmentation　datasets　and　gets　significant　improvements　compared　with　the　traditional　U-Net　framework,　as　well　as　the　latest　improved　ones.　Compared　to　the　baseline　network,　it　gets　the　improvements　of　0.93%　(Dice)　and　1.45%　(IoU)　on　GlaS,　2.09%　(Dice)　and　2.87%　(IoU)　on　MoNuSeg,　and　0.17%　(F1)　and　1.72%　(SE)　on　DRIVE.