YeastSAM: A deep learning model for accurate segmentation of yeast cells

Cell segmentation-the process of defining cell outlines in microscopy images-is essential for quantitative image analysis. Segmentation of budding yeast is challenging due to its asymmetric cell division and mother-bud morphology. Consequently, dividing cells are frequently misidentified as two separate cells, causing errors in downstream analysis. Here, we addressed this challenge by adapting the Segment Anything Model (SAM) to construct YeastSAM, a deep learning-based segmentation framework optimized for budding yeast. YeastSAM achieved more than three-fold higher accuracy in segmenting dividing cells compared with existing methods. When combined with single-molecule RNA imaging and organelle imaging, YeastSAM can be incorporated into a computational pipeline to build cellular maps. This enables quantitative analysis of the spatial regulation of gene expression. This study offers an accessible model for yeast cell segmentation, empowering researchers with minimal programming experience to perform quantitative image analysis.