This paper presents a novel technique for cell segmentation, named "Cellsketch," which generates an RGB mask containing simplified representations of cells (including nuclei, whole-cell, and cell boundaries) from microscopic images, and applies the watershed algorithm to produce segmentation masks of cells and nuclei. The RGB mask is generated using a generator model trained with a combination of L1 loss and adversarial loss. The method achieved accurate cell and nuclei segmentation from differential interference contrast (DIC) images using only automatically annotated training data and shows potential for a generalizable algorithm for cell segmentation. The code is freely available at: https://github.com/iranovianti/cellsketchClinical Relevance- This method simultaneously detects individual cells and nuclei from DIC images.