Check out this video to learn the process.

1. Drag and drop an image from a folder of images with a similar style (like similar cell types).
2. Run the built-in models on one of the images using the "model zoo" and find the one that works best for your data. Make sure that if you have a nuclear channel you have selected it for CHAN2.
3. Fix the labelling by drawing new ROIs (right-click) and deleting incorrect ones (CTRL+click). The GUI autosaves any manual changes (but does not autosave after running the model, for that click CTRL+S). The segmentation is saved in a "_seg.npy" file.
4. Go to the "Models" menu in the File bar at the top and click "Train new model..." or use shortcut CTRL+T.
5. Choose the pretrained model to start the training from (the model you used in #2), and type in the model name that you want to use. The other parameters should work well in general for most data types. Then click OK.
6. The model will train (much faster if you have a GPU) and then auto-run on the next image in the folder. Next you can repeat #3-#5 as many times as is necessary.
7. The trained model is available to use in the future in the GUI in the "custom model" section and is saved in your image folder.