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Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation

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. Motivation.

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Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation

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  1. Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation Ronell Sicat1, Markus Hadwiger1, Niloy Mitra1,2 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 segmentation proofreading analysis input

  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 distance region overlap

  13. Inconsistency Weight node distance region overlap

  14. Inconsistency Weight node distance region overlap

  15. Inconsistency Weight node distance region 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.

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