1. You have trained an Intellesis segmentation model.
2. Docker Desktop is installed together with the segmentation container and running.
3. You are in Free Mode.
4. You have loaded an image, for example via the Load File workbench. The image is visible in the image area.

1. Add the Image Processing workbench, click + Tool, and then double-click the Intellesis Segmentation entry.
   
2. Under Model select the desired model from the list. Ensure the model was trained on images with similar features; otherwise, segmentation may be ineffective. Additionally, the pixel types of the image and model must match, or the segmentation cannot be performed.
   
3. Select the desired Output Format.
   If you select Multi-Channel, the output will be a multi-channel image, with each class defined in the trained model represented in its own channel. This format can be easily viewed in the 3D view and seamlessly combined with the original image data.
   If you select Labels, the output will be a single-channel image, where pixels corresponding to different classes are labeled with distinct colors and represented by unique pixel values.
   Note: Currently such a label image cannot be displayed inside the 3D view directly without any further processing steps.
4. If necessary, adjust the Minimum Confidence slider. This will discard all pixels inside the resulting masks, where the confidence value is below the selected threshold.
5. Click Apply.
6. The automatic image segmentation using the Intellesis segmentation model is performed on the loaded image.
7. When the segmentation is finished, you get two resulting images depending on the output format:
   - the multi-channel or labels image and
   - the confidence map.