Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation
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![Page 1: Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation](https://reader036.fdocuments.in/reader036/viewer/2022062516/56812a84550346895d8e1d10/html5/thumbnails/1.jpg)
Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation
Ronell Sicat1, Markus Hadwiger1, Niloy Mitra1,2
1 King Abdullah University of Science and Technology2 University College London
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Motivation
• Extract 3D structures from electron microscopy (EM) data for analysis
• Target application: Connectomics
input segmentation proofreading analysis
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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 distance region overlap
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Inconsistency Weight
node distance region overlap
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Inconsistency Weight
node distance region overlap
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Inconsistency Weight
node distance region 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
![Page 20: Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation](https://reader036.fdocuments.in/reader036/viewer/2022062516/56812a84550346895d8e1d10/html5/thumbnails/20.jpg)
Automatic Correction for Special Case Errors
• Fixing extensions– average bounding box is
used for clipping– more complex bounding
region can be used
after
![Page 21: Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation](https://reader036.fdocuments.in/reader036/viewer/2022062516/56812a84550346895d8e1d10/html5/thumbnails/21.jpg)
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.