Software / Libraries


ilastik

dscho's picture

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings. The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

  • Object classification: Similar to pixel classification, but classifies previously segmented objects instead of making pixel-wise decisions
  • Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds
  • Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations
  • Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning
  • Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif

License: 
GPLv2
References: 
http://dx.doi.org/10.1109/ISBI.2011.5872394
Ecosystem: 
Python
Platform(s): 
Windows, Linux, Mac
Target Audience(s): 
Biologist, Analyst
Type(s): 
Application
Interoperates with: 
CellProfiler
Rating: 
5
Average: 5 (1 vote)