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Lesion Penumbra Selection

Lesion Penumbra Selection. Last Time. Previously, Brian suggested using a z -score threshold of > 2 as perhaps a way to select lesion penumbra/DAWM

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Lesion Penumbra Selection

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  1. Lesion Penumbra Selection

  2. Last Time • Previously, Brian suggested using a z-score threshold of > 2 as perhaps a way to select lesion penumbra/DAWM • We saw that this was effective and would at the very least reduce a lot of the work required by a radiologist reviewing the original lesion segmentation • Not sure yet if Michael approves of the overall DAWM selection quality with this technique

  3. Last Time • We saw that all of the thresholding methods (before or after warping) yield very similar lesion boundaries • Notably, thresholding at 0.5 after warping the “> 4 masks” edited by Hagen to patient space is very similar to the results with the more “review-board-safe” method of thresholding the z-score maps in patient space • This similarity allows us to recover many of Hagen’s edits by applying the penumbra selection method to them • We decided that warping the z-score maps made the most sense rather than warping the mean and standard deviation maps and calculating z-score patient space

  4. Penumbra Selection Method • Brute force approach – used for recovering Hagen’s edits • Input: 2 masks (lesion core and penumbra, or edited >4 MNI and new >4 patient space) • Assign a number label to each contiguous region in the masks (bwlabeln) • Go through all the labels in one mask and see if there’s an entity in the other mask that overlaps with it • If there is, keep it; else, discard.

  5. Penumbra Selection Method • Faster intelligent approach – used for keeping penumbra with lesions cores • Can take advantage of the fact that >2 masks will entirely encompass >4 masks • Assign a number label to each contiguous region in the masks (bwlabeln) • For each labels in one mask, find the overlapping entity in the other mask (this is no longer an if) • Keep it and subtract the entity from the first mask, i.e. remove all cores contained in that penumbra • Then relabel the first mask and restart the for loop • About a 2x speed increase, greater for patients with large abnormalities

  6. Results – Penumbra Selection • Color coding: • Yellow– core • Red – selected penumbra with cores • Green – left over penumbra

  7. P009– Low CIS – FLAIR

  8. P009– Low CIS – Penumbra

  9. P015 – Low CIS – FLAIR

  10. P015 – Low CIS – Penumbra

  11. Results – Hagen’s Edits • Color coding: • Red– new lesion core from thresholding in patient space that overlapped with one of Hagen’s • Blue – this should be out-of-brain tissue

  12. P009– Low CIS – FLAIR

  13. P009– Low CIS – Hagen’s Edits

  14. P015 – Low CIS – FLAIR

  15. P015 – Low CIS – Hagen’s Edits

  16. Discussion • Overall, I think the selection method has merit • It does a decent job at carrying over Hagen’s work but clearly there are some spots excluded that should not be • Penumbra selection works as imagined, still need expert eyes to determine if it’s worthwhile

  17. Future • 3D visualizations? • A tool to easily choose which lesions are good or bad with one-click

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