Cell image segmentation and counting are widely used in medical research. Segmenting adherent cell images is a challenging problem. This paper proposes a two-stage cell segmentation algorithm based on morphology and convex hull defect detection. UNet++ is firstly used to segment the cell image. Then, for the cell image that still has adhesion after segmentation, the method based on morphology or convex hull defect detection, a technique that identifies concave regions in the cell image, is used to segment the cell image more finely to eliminate the adhesion between cells and provide a more accurate basis for cell image counting. The experimental results show that cell counting accuracy after using the two-stage methods reaches 91.66% and 93.35%, respectively, 2.495% and 4.185% higher than that obtained by UNet++ alone.