Nuclei　segmentation　plays　an　important　role　in　cancer　diagnosis.　Automated　methods　for　digital　pathology　become　popular　due　to　the　developments　of　deep　learning　and　neural　networks.　However,　this　task　still　faces　challenges.　Most　of　current　techniques　cannot　be　applied　directly　because　of　the　clustered　state　and　the　large　number　of　nuclei　in　images.　Moreover,　anchor-based　methods　for　object　detection　lead　a　huge　amount　of　calculation,　which　is　even　worse　on　pathological　images　with　a　large　target　density.　To　address　these　issues,　we　propose　a　novel　network　with　an　anchor-free　detection　and　a　U-shaped　segmentation.　An　altered　feature　enhancement　module　is　attached　to　improve　the　performance　in　dense　target　detection.　Meanwhile,　the　U-Shaped　structure　in　segmentation　block　ensures　the　aggregation　of　features　in　different　dimensions　generated　from　the　backbone　network.　We　evaluate　our　work　on　a　Multi-Organ　Nuclei　Segmentation　dataset　from　MICCAI　2018　challenge.　In　comparisons　with　others,　our　proposed　method　achieves　state-of-the-art　performance.　©　2021　ACM.