After selecting one of the segmentation methods and clicking Create Segmentation, the segmentation is applied to the entire gray value image. A result file (*.gdr) is generated and saved automatically to the project folder. The result file with the name entered in the Result File Name field opens automatically in the Result Viewer.

The Report tab of the result file contains the following information:

• The applied Segmentation method
• The applied Image Processing and Filtering steps
• Structure Information, such as voxel length, size of the dataset, contained materials and their volume percentages

In addition to the result file, a result folder with the same name is saved in the current project folder. This folder contains the segmented structure file (*.gdt). For AI segmentation, it also contains the used labels (*.gld), and the trained AI model (*.FOREST, *.XGBM, *.UNET2D, or *.UNET3D).

Refer to the Result Viewer user guide for a more detailed description of the Result Viewer options.

If you trained your model using one of the Unet models, you can load the model's confidence field under the Data Visualization in the Result Viewer. The confidence field represents the model’s estimated certainty for each pixel belonging to a specific material. These values can help you to identify regions in the image that are uncertain or ambiguous, which can be improved by adding more labels and continuing to train the model.