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Jul 15, 2013 Jason Su

Jul 15, 2013 Jason Su. Motivation. Manual segmentation of structures on MR images is an important but often tedious task, requiring the expertise of a radiologist Automation is highly desirable and can also help remove observer bias

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Jul 15, 2013 Jason Su

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  1. Jul 15, 2013 Jason Su

  2. Motivation • Manual segmentation of structures on MR images is an important but often tedious task, requiring the expertise of a radiologist • Automation is highly desirable and can also help remove observer bias • At 7T, we are working on studies that could benefit from this • Manually segmenting thalamic nuclei with the improved contrast of WMn-MPRAGE • Measuring atrophy in MS patients

  3. Background

  4. Similarity Measures

  5. Similarity Measures

  6. STEPS Improvements on STAPLE

  7. Theory: Model

  8. Theory: Model

  9. Improvement: Local Ranking

  10. Improvement : MRF Regularization

  11. Improvement: Unbiasing

  12. Validation

  13. Results: Optimizing Local Ranking • STEPS does best for X=15 but the gain is marginal? • Notably, other algorithms can become much worse with more templates

  14. Results: Simulation • Local ranking in STEPS beats global ranking in STAPLE • Especially when the morphology of the target is very different from atlas raters • This means can get away with less raters to cover more cases

  15. Results: Phantom Simulation • Measure the performance as R, the number of raters or size of the atlas, is reduced • STEPS beats the others with even the smallest R • Performance characteristics can change with R, i.e. optimal X

  16. Results: Phantom Simulation • STEPS is significantly better than all of these competitors • They all seem pretty good though, splitting hairs?

  17. Results: Phantom Simulation

  18. Results: ADNI

  19. Results: MRF Smoothing in Multi-Steps

  20. Discussion

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