Aiming at the problems of low contrast, uneven background and halo artifacts in the images of mesenchymal stem cells collected by the phase contrast microscope, this paper proposes a cell image segmentation method combined with anti-background subtraction and Otsu. The method constructs anti-background subtraction to enhance the difference between the cell body and the noncellular area and reduce the influence of uneven background, combines the Otsu threshold segmentation method to roughly distinguish the cells and the background, and further corrects the segmentation results by a combination of algorithms including binary morphology operations, image filtering, and local gradient iteration. The four evaluation indexes of pixel accuracy, intersection over union, dice similarity coefficient, and confluency error achieved values of 0. 933 8, 0. 729 6, 0. 852 4, and 0. 07, respectively, by segmentation validation on the actually acquired cell images. The results indicate that the algorithm has high segmentation performance, can objectively, accurately and automatically analyze the confluency of cells, and can process images of cells in different culture periods, which has high application value.