Accurate　cell　segmentation　and　counting　play　an　important　role　in　medical　diagnosis.　However,　the　size　and　shape　of　cells　are　varied　largely,　and　the　presence　of　overlapping　cells　complicates　cell　counting.　Recent　studies　have　shown　that　multi-task　learning　methods　perform　well　in　deep　learning.　In　specific,　we　design　Multi-task　Segmentation　Regression　Counting　Network　(MSRCN).　For　cell　segmentation,　a　multi-scale　attention　mechanism　module　is　designed　to　suppress　irrelevant　regions　and　learns　salient　features　for　a　specific　task.　For　cell　counting,　a　regression　model　is　utilized　to　learn　a　mapping　from　cell　feature　information　to　target　counts.　The　proposed　MSRCN　model　is　analyzed　and　compared　with　other　states　of　the　art　cell　segmentation　methods　and　cell　counting　methods.　MSRCN　outperforms　these　methods　in　all　evaluation　metrics.　The　Dice　similarity　coefficient,　root　mean　square　error,　and　mean　absolute　error　of　the　proposed　method　is　0.9316,　2.1215,　and　1.5927,　respectively.　The　experiments　results　show　that　the　proposed　method　not　only　improves　the　functioning　of　cell　segmentation,　but　also　outperforms　direct　regression　counting　methods　in　terms　of　cell　counting.　©　2022　ACM.