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

Motivation

• Extract 3D structures from electron microscopy (EM) data for analysis

• Target application: Connectomics

input segmentation proofreading analysis

Input

• EM scans of mouse cortex (1024 x 1024 x 150 slices )

Segmentation

• Automatic segmentation extracts neural structures (not perfect)

Proofreading

• Search for and correct segmentation errors

Analysis

• Segmented 3D structures are visualized and analyzed

Motivation

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

Graph Abstraction of Segmentation Data

• Node– segmented region– center of mass

• Edge– connected regions

(same object)

Graph Abstraction of Segmentation Data

Inconsistency Weight

node distance

Inconsistency Weight

node distance

Inconsistency Weight

node distance region overlap

Inconsistency Weight

node distance region overlap

Inconsistency Weight

node distance region overlap

Inconsistency Weight

node distance region overlap

Error Visualization using Inconsistency Weights

Directing the User to Error Regions

Automatic Correction for Special Case Errors

• Fixing extensions– average bounding box is

used for clipping– more complex bounding

region can be used

before

Automatic Correction for Special Case Errors

• Fixing extensions– average bounding box is

used for clipping– more complex bounding

region can be used

before

Automatic Correction for Special Case Errors

• Fixing extensions– average bounding box is

used for clipping– more complex bounding

region can be used

after

Automatic Correction for Special Case Errors

• Fixing holes– fill hole if present in both

neighbor regions– more sophisticated

methods can be used

before

Automatic Correction for Special Case Errors

• Fixing holes– fill hole if present in both

neighbor regions– more sophisticated

methods can be used

after

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

Automatic Correction (single node)

Manual Correction (single node)

Automatic Correction (all nodes)

Final Result

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

Thank you!

Inconsistency Weight Equations

Segmentation Details

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

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.