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Aligner for large scale serial section image data

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Aligner

What it does well

Stitches together any mode of serial section image data:

  • TEM
  • Block-face SEM
  • Fluorescence Array Tomography

Fast and scalable:

  • Runs on linux cluster using Sun Grid Engine API
  • Align 2 to billions of images
  • Approx. linear time/volume & mem/volume scaling
  • Two million 4MB images align in about 8 man-hours

Handled pathologies:

  • Missing tiles or whole sections
  • Fragmented / small / irregular sections
  • Arbitrarily rotated/translated sections
  • Burns, scars, foreign matter
  • Exposure inhomogeneity

Input:

  • 8 or 16 bit TIF, PNG, MRC
  • Simple meta-data as text or TrakEM2 XML

Output:

  • Basically 1 affine or homographic transform per image tile
  • Flexible output as text tables or TrakEM2 XML files

Limitations

  • One linear transform / tile; not an elastic aligner
  • Unfinished handling of geometry-altering folds and tears
  • All images in a data set must share the same X and Y dimensions, though X need not equal Y

Requirements

  • Linux cluster with (Sun / Oracle / Univa) Grid Engine.
  • Fiji + TrakEM2: invaluable image and stack tools.

Documentation

  • Project folder 00_DOC details installation and methods
  • Alignment_Tutorial walks you through a real-world example

Authorship

Developed over several years at HHMI/Janelia Research Campus, originally by Louis Scheffer, and subsequently refined into current form by Bill Karsh. See reference "Automated Alignment of Imperfect EM Images for Neural Reconstruction".

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Aligner for large scale serial section image data

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  • C++ 86.2%
  • C 10.5%
  • Shell 1.6%
  • Makefile 0.9%
  • Python 0.6%
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