Cellpose 2.0: how to train your own model.

Our Research / Publications

janelia7_blocks-janelia7_biblio_header | block

Nature Methods. 2022 Nov 07;19(12):1634-41. doi: 10.1038/s41592-022-01663-4

View Article on

Cellpose 2.0: how to train your own model. Pachitariu LabStringer Lab

Pachitariu M, Stringer C

janelia7_blocks-janelia7_biblio_abstract | block

Abstract

Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500-1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100-200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

PMID: 36344832 [PubMed - indexed for MEDLINE]

node:body | entity_field

bioRxiv PrePrint https://doi.org/10.1101/2022.04.01.486764

janelia7_blocks-janelia7_biblio_authors | block

janelia7_blocks-janelia7_biblio_tools | block

Main Menu (Mobile)- Block

Main Menu - Block

Cellpose 2.0: how to train your own model.

Abstract

PMID: 36344832 [PubMed - indexed for MEDLINE]

Janelia Authors