1 - Data

Required Steps:

1. Select image folder
   • Select model ensemble folder if not default
2. Click Load
3. For evaluation: Select* masks folder (same requirements as for training.)

Evaluation (hold-out test set)

New data (no masks available)

Input Details:

• One folder for images
  • Images must have unique name or ID
    • _0001.tif --> name/ID: 0001; img_5.png --> name/ID: img5, ...
  • Same number of channels as training images (e.g., 1 greyscale; 3 RGB)
• For evaluation: Corresponding masks must start with name or ID + a mask suffix
  • same requirements as for training
• One folder containing trained models (ensemble)
  • Ensemble folder and models will be created during Training
    • Do not change the naming of the models
    • If you want to train different ensembles, simply rename the ensemble folder

Examplary input folder structure:

──images            -> one image folder
  │   0001.tif      
  │   0002.tif

──masks             -> one masks folder (evaluation only)
  │   0001_mask.png
  │   0002_mask.png

──ensemble          -> one model folder
  │   Unet_resnet34_2classes-fold1.pth
  │   Unet_resnet34_2classes-fold2.pth

2 - Prediction and Quality Control

Required Steps:

1. Click Run Prediction

Evaluation (hold-out test set)

New data (no masks available)

3 - Cellpose Instance Segmentation

Required Steps:

1. Click Run Cellpose

Evaluation (hold-out test set)

New data (no masks available)