Batch Processing with Lung CT Analyzer

Use Lung CT Analyzer's batch processing function for easy sequential lung analysis of pre-segmented lung CT with a few mouse clicks and the following workflow:

• Select appropriate thresholds
• Input lung volume and input segmentation will be automatically detected and must not be selected.
• Check "Region analysis" if required
• Check "Lobe analysis" if required
• Select the batch processing input folder
• Select the batch processing output folder
• Enable "Testmode" if required (only 3 datasets will be analyzed)
• Disable "Testmode" if all subfolders of the input directory should be processed (this may take a long time)
• If "Load input files only" is checked, no analysis will be performed and the batch routine will just cycle through the datasets.
• If "Use calibrated CT" is checked, batch processing will do the analysis with the calibrated CT dataset (must be generated during Lung CT Segmenter batch processing), otherwise with the raw input data.
• Press "Batch process"

Remarks: Raw data must be batch-preprocessed with Lung CT Segmenter and AI to generate the corresponding MRB files. All datasets below the input folder will be analyzed with identical thresholds.

All CT input files present in the input directory subfolders will be processed one by one. In "Testmode", only three datasets will be processed.

• Current scene will be closed
• The next MRB file with an CT dataset and Lung Segmentation will be loaded
• Lung analysis will be performed
• Results will be saved to the output folder (MRB [optional] and CSV files)
• Execution times will be measured.

3D Slicer will be not responsive until batch processing has finished, which may take hours. If you need to cancel the process please press the cancel button a few times.

Progress can be monitored in the Python Console or the Status Message region:

The structure of the input folder should contain one immediate subdirectory for each dataset. In each subdirectory, a "CT_seg.mrb" file is expected. Input files that are placed into the input folder directly (without subdirectories) will be ignored.

The structure of the input folder will be mirrored in the output folder during processing.

CSV data files will be automatically generated.

The content of an output subfolder of the output folder could look like this: