To address the issue of cell segmentation challenges caused by weak edges, uneven backgrounds, and irregular cell shape in brightfield microscopic images, we suggest a cell segmentation method for brightfield microscopic images based on fluorescent nucleus guidance. First, the fluorescent nuclear centroid determines the local microscopic image of a single cell in the brightfield, the double Gaussian filtering reduces the impact of nonuniform background, the top-hat transform enhances the contrast of the images, and the two-dimensional maximum interclass variance segmentation method enhances the antinoise performance of the algorithm. Second, the complete brightfield microscopic cell image is preprocessed using double filtering and top-hat transformation. This is followed by global segmentation using the two-dimensional maximum interclass variance method to enhance the lost cell contour information in local segmentation, which is beneficial to solve the inaccurate segmentation problem caused by irregular cell shape. To increase the segmentation accuracy of sticky cells when local and global findings are combined, the watershed transformation is then employed for secondary segmentation. Through the verification experiment on the Hela cell image set, the accuracy, recall rate, and F value of the brightfield cell segmentation are 0.960, 0.984, and 0.971, respectively, which are better than the existing algorithms; the results confirm the high accuracy and robustness of the proposed method.