Segmentation (Deep Learning)

Training and Using Segmentation Models

The Batchtool allows you to train custom 3D segmentation models on your own data and apply them to new studies.

Here's a short tutorial on how to use the patchwork segmentation model (Deep Neural Patchworks: Coping with Large Segmentation Tasks, Reisert et. al, 2022)

1. Training a Segmentation Model on Your Data

In the Batchtool:

• Create a new Analysis → ML → Patchwork 3D

Entry fields:

• Inputs contrasts and Target labels: Enter the filenames of your images and masks as they appear in each study
• Model: Select an analysis folder (create one in your project if needed) and provide a name for your model folder, for example: isles2022_stroke

Configuration:

• In the Studies panel, check the studies you want to train the model on
• Select a job queue and run the analysis
• The job should appear in the Gridstats. You can monitor training progress in the log.

Training notes:

• The default number of iterations is 2500
• The model is saved after each epoch, so you can also terminate the job early if performance is satisfactory

2. Applying the Trained Model to New Studies

In the Batchtool:

• Create a new Processing → Image → Apply Patchwork

Entry fields:

• Contrast: The filename of your image in your studies
• Model: Select the same analysis folder and specify: <model_folder_name>/model_patchwork.json
  • For example: isles2022_stroke/model_patchwork.json
• Output type: Select Mask
• Output: Specify the desired name for the resulting mask

Configuration:

• In the Studies panel, check the studies you want to apply the model to
• Select a job queue and run the processing
• Monitor the job progress through the logs in Gridstats

Detailed documentation :