time-series

This workflow classifies, or segments, the pixels of an image given user annotations. It is especially suited if the objects of interests are visually (brightness, color, texture) distinct from their surrounding. Users can iteratively select pixel features and provide pixel annotations through a live visualization of selected feature values and current prediction responses. Upon users' satisfaction, the workflow then predicts the remaining unprocessed image(s) regions or new images (as batch processing). Users can export (as images of various formats): selected features, annotations, predicted classification probability, simple segmentation, etc. This workflow is often served as one of the first step options for other workflows offered by ilastik, such as object classification, automatic tracking.

Requires Matlab Runtime Environment or Matlab. Source code (m-files) are downloaded. Software availability: AVeMap was developed under MATLAB (MathWorks). It is available as an executable, multiplatform program, together with source codes and documentation here, and the source code is also available as Supplementary Software. For practical reasons, this executable version, which does not require MATLAB, runs on a single processor. For users who want to customize the software and/or need the power of parallel computing, an installation of MATLAB with its 'parallel' and 'image processing' toolboxes is needed. Note that, even with the executable version, the velocity fields are stored for further analysis. The add-on AVeMap+ uses these AVeMap-computed velocity fields to generate heat map tables. It is available with the same link.

Analyzing Ca2+ sparks

ImageJ plugin to detect and measure Ca2+ sparks in linescan images, described in Picht et. al. (2007). The algorithm is based on that described by Cheng et al. (1999). Care should be taken to ensure that detections belong to 'true' events, as without any additional background subtraction steps the algorithm is not appropriate for images in which the baseline fluorescence varies substantially.

This workflow classifies objects based on object-level features (e.g. intensity based, morphology based, etc) and user annotations. It needs segmentation images besides the raw image data. Segmentation images can be obtained from ilastik pixel classification, or binary segmentation images from other tools. Within the object classification, one can prefilter objects through thresholds (on pixel probability image) or object sizes (on segmentation image). Outputs are predicted classification label images. Selected features can also be exported. Advanced users also have possibilities to add customized (object) features for classification in a simple plugin fashion through python scripts.

This workflow is used to track multiple (appear/disappear, dividing and merging) objects in presumably big 2D+t or 3D+t datasets. It is best suitable for roundish objects or spots. Tracking is done through segmentation, which can be obtained from ilastik pixel classification, or imported from other tools. Users should provide a few object level labels, and the software predicts results on the rest of the image or new images with similar image characteristics. As a result, all objects get assigned random IDs at the first frame of the image sequence and all descendants in the same track (also children objects such as daughter cells) inherit this ID.