Nuclei　instance　segmentation　is　a　critical　part　of　digital　pathology　analysis　for　cancer　diagnosis　and　treatments.　Deep　learning-based　methods　gradually　replace　threshold-based　ones.　However,　automated　techniques　are　still　challenged　by　the　morphological　diversity　of　nuclei　among　organs.　Meanwhile,　the　clustered　state　of　nuclei　affects　the　accuracy　of　instance　segmentation　in　the　form　of　over-segmentation　or　under-segmentation.　To　address　these　issues,　we　propose　a　novel　network　consists　of　a　multi-scale　encoder　and　a　dual-path　decoder.　Features　with　different　dimensions　generated　from　the　encoder　are　transferred　to　the　decoder　through　skip　connections.　The　decoder　is　separated　into　two　subtasks　to　introduce　boundary　information.　While　an　aggregation　module　of　contour　and　nuclei　is　attached　in　each　decoder　for　encouraging　the　model　to　learn　the　relationship　between　them.　Furthermore,　this　avoids　the　splitting　effect　of　independent　training.　Experiments　on　the　2018　MICCAI　challenge　of　Multi-Organ　Nuclei　Segmentation　dataset　demonstrate　that　our　proposed　method　achieves　state-of-the-art　performance.　©　2020,　Springer　Nature　Switzerland　AG.