ProjectWeek200706:LesionClassificationInLupus

Return to Project Week Main Page

Key Investigators

• MIND/UNM:
  • Jeremy Bockholt
  • Mark Scully
  • Charles Gasparovic
• Core 1
  • Bruce Fischl
  • Guido Gerig
• External Collaborator
  • Vincent Magnotta

Objective

Our goal is to automatically, or with little or no manual human rater input, accurately tissue classify our example lupus data-set into gray, white, csf, and lesion classes.

Approach, Plan

Our approach is to utilize existing automatic lesion classification techniques, both within NA-MIC kit and external to it.

Our plan for the project week is to learn and use EMSegment (S. Wells), BRAINS2 (V. Magnotta), and Constructing Image Graphs for Lesion Segmention (M. Prastawa)". We will learn and begin to compare and contrast these approaches with a bronze standard (manual rater tracings) of lesions.

Progress

This section will get filled out at the end of the Project week.

Additional Information

• Homepage for the MIND/UNM DBP (II)

Using an exemplar case that has already been processed using non-NAMIC kit tools:

1. Use existing NA-MIC kit to coregister T1, T2, Flair, CBV, CBF, and MTT
2. Use the EM Segment in slicer 2-3.X to classify grey, white, csf, and white matter lesion
3. Summarize the volume and location of lesions
4. Measure the intensity of co-registered CBV, CBF, and MTT images within lesion and meaningful non-lesion regions of the brain
5. If time permits, co-register DTI data and generate scalars such as FA, MD, RD, and AD
6. If time permits, measure DTI scalars within normal white matter and within white matter lesions.