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SegError

SeungLab Error Metrics for Volummetric Segmentation

This module computes the error between two segmentations of a volume. The metrics are either based on the Rand Index or Variation of Information, and features a few customizable options. These include:

  • Foreground Restriction- computing error over voxels where the second segmentation (presumed to be ground truth) != 0. This is applied as a default option.

  • Splitting '0' Segment into Singletons- The segment id of 0 often corresponds to a background segment, however it can also represent singleton voxels after processing by watershed. This option re-splits all segments labelled 0 into new segment ids, recovering the singletons. This is applied as a default option, and disabled by -no_split0

  • Boundary Thinning- (not complete yet)

Inputs:

  • First Segmentation File (seg1, as .tif file)

  • Second Segmentation File (seg2, as .tif file) This should be the "ground truth" segmentation if applicable

  • Foreground Restriction (optional flag -nofr, default=on)

  • Boundary Thinning (not complete yet)

  • Split 0 Segment (optional flag -no_split0, default=on)

  • Metric Types (all calculated by default)

  • Rand F Score - ISBI 2012 Error Metric

  • Rand Error - 1 - RandIndex

  • Variation F Score - ISBI 2012 Information Theoretic Error Metric

  • Variation of Information

  • 2D Metric Types (not calculated by default)

    • Rand F Score -rfs2d
    • Rand Error -re2d
    • Variation F Score -vifs2d
    • Variation of Info -vi2d

Dependencies:

Library
Cython >= 0.23.4
python.tifffile
NumPy
Scipy

Installation (compiling Cython module):

make

NOTE: You will see a harmless warning when compiling the Cython functions. See (http://docs.cython.org/src/reference/compilation.html)

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