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.

用于生物分割的预训练神经网络模型可以为许多图像类型提供开箱即用的良好结果。然而，此类模型不允许用户根据他们的特定需求调整分割样式，并且对于与训练图像截然不同的测试图像可能表现不佳。在这里，我们介绍 Cellpose 2.0，这是一个新的软件包，其中包括各种预训练模型的集合以及用于快速制作新自定义模型原型的人在回路管道。我们表明，在 Cellpose 数据集上预训练的模型可以仅使用 500–1,000 个用户注释的感兴趣区域 (ROI) 进行微调，其性能几乎与在整个数据集上训练的模型一样好，ROI 高达 200,000。人在环路方法进一步将所需的用户注释减少到 100-200 ROI，同时保持高质量的分割。