Projects:ARRA:SlicerWF:UseCaseScenarios

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Overview

• Oct-20-2009
• Meeting with Charles and Alex. Discussed following use-case scenario:
  • Data lives in XNAT
  • Black box processing pipeline applied to data
    • Results back to XNAT after QC for completeness
    • MRML file gets autogenerated
  • Data loaded into Slicer for interactive editing of label maps as QC
  • Results loaded back into XNAT

Use-case MS lesion Assessment

1. Workflow "blackbox" generates lesion maps along with lesion coordinates files
2. Human Interaction module loads image volumes into Slicer with fiduciary markers for lesion assessment
3. Detailed Scenario is described below

Step 1 Download Images from Xnat Convert T1, T2 images into Slicer3 format

Step 2 (preprocessing): Register T2 to T1

Step 3 (preprocessing)

Skull Stripping of T1 and T2_registered images

Step 3A . Generation of Brainmask/ICC

Step 3B. Manual Editing if Brainmask/ICC

Step 3C. Skull Stripping per se

Step 4 (preprocessing) Optional Intensity Normalization to the "gold standard" image(s) in case of automated runs

Step 5 Generate EM Segmentation Scene from the template.

Step 6 Load The scene into Slicer3

Step 7 Adjust input parameters if needed. Train or re-train data for intensity distribution

Step 8 Run EM Segmenter

Step 9 Repeat Steps 7 and 8 if needed

Step 10 Save EM Segmented Label Map Volume with Feducials' coordinates

Step 11 Visual Lesion Assessment

Step 12 Upload Results to XNat

Use-case COPD

External use-cases

• see also Hans Johnsons scenario for similarities.