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An open-source toolkit for conditional random field (CRF) and dense CRF

Project description


Matlab and Python wrap of Conditional Random Field (CRF) and fully connected (dense) CRF for 2D and 3D image segmentation, according to the following papers:

[1] Yuri Boykov and Vladimir Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision", IEEE TPAMI, 2004.

[2] Philipp Krähenbühl and Vladlen Koltun, "Efficient inference in fully connected crfs with gaussian edge potentials", in NIPS, 2011.

[3] Kamnitsas et al in "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation", Medical Image Analysis, 2017.

maxflow densecrf1 densecrf2


This repository depends on the following packages: Maxflow, DenceCRF and 3D Dense CRF


  1. Install by: pip install SimpleCRF

  2. Alternatively, you can compile the source files by the following two steps:

python build
python install


Some demos of using this package are:

  • examples/ using maxflow for automatic and interactive segmentation of 2D and 3D images.

  • examples/ using dense CRF for 2D gray scale and RGB image segmentation.

  • examples/ using 3D dense CRF for 3D multi-modal image segmentation.


  1. maxflow has four functions as follows. Note that the current version only supports binary segmentation.
  • maxflow.maxflow2d() for 2D automatic segmentation.

  • maxflow.interactive_maxflow2d() for 2D interactive segmentation.

  • maxflow.maxflow3d() for 3D automatic segmentation.

  • maxflow.interactive_maxflow3d() for 3D interactive segmentation.

  1. denseCRF has the following function. It can deal with multi-class segmentation, and only supports RGB images.
  • denseCRF.densecrf() for 2D automatic segmentation.
  1. denseCRF3D has the following function. It can deal with multi-class segmentation. The input channel number can be 1-5.
  • denseCRF3D.densecrf3d() for 3D automatic segmentation.

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