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Published byMagnus Waters Modified over 9 years ago
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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
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Motivation Extract 3D structures from electron microscopy (EM) data for analysis Target application: Connectomics input segmentationproofreadinganalysis
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Input EM scans of mouse cortex (1024 x 1024 x 150 slices )
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Segmentation Automatic segmentation extracts neural structures (not perfect)
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Proofreading Search for and correct segmentation errors
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Analysis Segmented 3D structures are visualized and analyzed
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Motivation Proofreading – tedious and time consuming We want abstraction of segmentation data – cheap to compute – provides search and correction support
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Graph Abstraction of Segmentation Data Node – segmented region – center of mass Edge – connected regions (same object)
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Graph Abstraction of Segmentation Data
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Inconsistency Weight node distance
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Inconsistency Weight node distance
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Inconsistency Weight node distanceregion overlap
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Inconsistency Weight node distanceregion overlap
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Inconsistency Weight node distanceregion overlap
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Inconsistency Weight node distanceregion overlap
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Error Visualization using Inconsistency Weights
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Directing the User to Error Regions
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Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used before
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Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used before
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Automatic Correction for Special Case Errors Fixing extensions – average bounding box is used for clipping – more complex bounding region can be used after
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Automatic Correction for Special Case Errors Fixing holes – fill hole if present in both neighbor regions – more sophisticated methods can be used before
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Automatic Correction for Special Case Errors Fixing holes – fill hole if present in both neighbor regions – more sophisticated methods can be used after
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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
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Automatic Correction (single node)
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Manual Correction (single node)
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Automatic Correction (all nodes)
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Final Result
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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
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Thank you!
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Inconsistency Weight Equations
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Segmentation Details Segmentation algorithm - Kaynig, V., Fuchs, T., Buhmann, J. M., Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images, CVPR, 2010.
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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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