  Cells are the fundamental unit of biological organization, and identifying
them in imaging data - cell segmentation - is a critical task for various
cellular imaging experiments. While deep learning methods have led to
substantial progress on this problem, models that have seen wide use are
specialist models that work well for specific domains. Methods that have
learned the general notion of "what is a cell" and can identify them across
different domains of cellular imaging data have proven elusive. In this work,
we present CellSAM, a foundation model for cell segmentation that generalizes
across diverse cellular imaging data. CellSAM builds on top of the Segment
Anything Model (SAM) by developing a prompt engineering approach to mask
generation. We train an object detector, CellFinder, to automatically detect
cells and prompt SAM to generate segmentations. We show that this approach
allows a single model to achieve state-of-the-art performance for segmenting
images of mammalian cells (in tissues and cell culture), yeast, and bacteria
collected with various imaging modalities. To enable accessibility, we
integrate CellSAM into DeepCell Label to further accelerate human-in-the-loop
labeling strategies for cellular imaging data. A deployed version of CellSAM is
available at https://label-dev.deepcell.org/.
