Figure 5.

–Machine-Learning and Manual Validation Overview

(A) Machine-learning overview. Upon selection of the images to use as training set, the user interactively selects first the pluripotent colonies, then the mixed colonies and, finally, the differentiated colonies presented in the image. After this procedure is complete for all the images in the training set, the classifier is built, with the possibility to add the training set to a previous classifier, or create a new training set. An Excel file with all the colony features as well as the pluripotency score is created, where score 1 is differentiated, 2 mixed, and 3 pluripotent colony. Green, pluripotent marker AP; red, colony border obtained by the phase-contrast segmentation image.

(B) Manual validation overview: (B1) image overview. Purple arrow shows an example of a pluripotent colony. Red arrow shows the colony picked to validate the classifier prediction. (B2) Phase-contrast image and (B3) pluripotent marker image of the colony picked to validate the classifier prediction. (B4) Example of a pluripotent colony. (B5) Classifier prediction and progress bar with the total number of colonies present in the image and the number of colonies already validated. (B6) Normalized number of pixel versus pluripotent marker intensity. (B7) Graphical user interface used to validate each colony as pluripotent, mixed, or differentiated. (B8) After manual validation completed, the manual accuracy is automatic shown, and the random forest algorithm is updated. Segmentation information summary is saved as an Excel file.

See Table S1.