Solving cellular segmentation with limited data with Cellpose
Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. I will also cover Cellpose 2.0, a new package which includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for quickly prototyping new specialist models. We show that specialist models pretrained on the Cellpose dataset can achieve state-of-the-art segmentation on new image categories with very little user-provided training data.
Carsen Stringer is a group leader at HHMI Janelia Research Campus. She did her PhD at University College London in computational neuroscience, and her postdoc at Janelia. Her lab develops machine learning tools for interpreting large-scale neural, behavioral, and imaging data.