Fig. 4. Segmentation evaluation of Self-Supervised Machine Learning (SSL) and CellPose on the data sets used for validation in this study via F₁-scores.

The top row includes the name of the data set annotated by magnification, optical modality, cell type and brief description of the imagery characteristics. #L stands for the number of annotated labels used for model training, and #O stands for the number of objects to be segmented by the model within a given data set. *CellPose has a single parameter, a size filter, that can be automatically estimated, however, for some of the data sets the best segmentation was found by manually tuning this size filter. The figures below show the ground truth (green-solid lines), SSL (cyan-large dashes), and CellPose (red-small dashes) outlines overlaid on the final image of the data set.