Check out this video to learn the process.
- Drag and drop an image from a folder of images with a similar style (like similar cell types).
- 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.
- 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.
- Go to the "Models" menu in the File bar at the top and click "Train new model..." or use shortcut CTRL+T.
- 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.
- 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.
- 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.