Habitat

Ecology

Biology

Materials science

Retraining

Range (aeronautics)

3d model

Computer science

Business

International trade

Image segmentation

Segmentation

Pattern recognition (psychology)

Generalist and specialist species

Artificial intelligence

Composite material

Deep learning

Training set

Machine learning

Computer vision

Nature Methods

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 and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

Cellpose: a generalist algorithm for cellular segmentation