1. Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation Ronell Sicat 1, Markus Hadwiger 1, Niloy Mitra 1,2 1 King Abdullah University of Science and Technology 2 University College London

2. Motivation Extract 3D structures from electron microscopy (EM) data for analysis Target application: Connectomics input segmentationproofreadinganalysis

3. Input EM scans of mouse cortex (1024 x 1024 x 150 slices )

4. Segmentation Automatic segmentation extracts neural structures (not perfect)

5. Proofreading Search for and correct segmentation errors

6. Analysis Segmented 3D structures are visualized and analyzed

7. Motivation Proofreading – tedious and time consuming We want abstraction of segmentation data – cheap to compute – provides search and correction support

8. Graph Abstraction of Segmentation Data Node – segmented region – center of mass Edge – connected regions (same object)

9. Graph Abstraction of Segmentation Data

10. Inconsistency Weight node distance

11. Inconsistency Weight node distance

12. Inconsistency Weight node distanceregion overlap

13. Inconsistency Weight node distanceregion overlap

14. Inconsistency Weight node distanceregion overlap

15. Inconsistency Weight node distanceregion overlap

16. Error Visualization using Inconsistency Weights

17. Directing the User to Error Regions

18. Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used before

19. Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used before

20. Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used after

21. Automatic Correction for Special Case Errors Fixing holes – fill hole if present in both neighbor regions – more sophisticated methods can be used before

22. Automatic Correction for Special Case Errors Fixing holes – fill hole if present in both neighbor regions – more sophisticated methods can be used after

23. Automatic Correction for Special Case Errors Not perfect (reduces manual effort needed) Automatic correction (with threshold) – all threads – one thread – one node Manual correction can be done anytime Proofreading tool is implemented as Avizo plugin

24. Automatic Correction (single node)

25. Manual Correction (single node)

26. Automatic Correction (all nodes)

27. Final Result

28. Conclusion Graph abstraction of segmentation data – very cheap to compute – helps in visualization – directs user to error regions – simple but provides fast method for reducing special case errors

29. Thank you!

30. Inconsistency Weight Equations

31. Segmentation Details Segmentation algorithm - Kaynig, V., Fuchs, T., Buhmann, J. M., Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images, CVPR, 2010.

32. Tracing Details 3D tracing (Euclidean distance of region center, overlap, difference in region size, texture similarity, smooth continuation) - Kaynig, V., Fuchs, T., Buhmann, J. M., Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data, MICCAI, 2010.