Journal Article

Cellpose 2.0: how to train your own model

Stringer, Carsen, Pachitariu, Marius

2022

Overview

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. Cellpose 2.0 improves cell segmentation by offering pretrained models that can be fine-tuned using a human-in-the-loop training pipeline and fewer than 1,000 user-annotated regions of interest.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/1564 / 631/114/2398 / Annotations / Bioinformatics / Biological Microscopy / Biological Techniques / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Cytoplasm / Datasets / Graphical user interface / Humans / Image Processing, Computer-Assisted - methods / Image segmentation / Labeling / Life Sciences / Machine learning / Microscopy / Neural networks / Neural Networks, Computer / Proteomics / Rapid prototyping / Software / Software utilities / Training / User interface / Vision systems