Windows

A python package for background and shading correction of optical microscopy images

BaSiCPy is a python package for background and shading correction of optical microscopy images. It is developed based on the Matlab version of BaSiC tool with major improvements in the algorithm.

Colocalization Colormap –an ImageJ Plugin for the Quantification and Visualization of Colocalized Signals

This ImageJ plugin implements the Jaskolski's algorithm (Jaskolski et al. 2005). It creates a pseudo-color map of correlations between pairs of corresponding pixels in two original input images. With it one can quantitativly visualize colocalization.

This plugin is able to stitch an arbitrary collection or grid of images, it does not matter if it is 2d, 3d, 4d or 5d images as long as all images are of the same type. In contrast to the Pairwise Stitching of two images, this plugins will load (and potentially save) the images from/to harddisc.

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The Pairwise Stitching first queries for two input images that you intend to stitch. They can contain rectangular ROIs which limit the search to those areas, however, the full images will be stitched together. Once you selected the input images it will show the actual dialog for the Pairwise Stitching.

3DeeCellTracker is a deep-learning based pipeline for tracking cells in 3D time-lapse images of deforming/moving organs.

The installation comprises a set of Jupyter notebooks and a library they depend on. The workflow steps include separate training and segmentation/tracking.

Examples of cell tracking from the reference publication are: ~100 cells in a freely moving nematode brain, ~100 cells in a beating zebrafish heart, and ~1000 cells in a 3D tumor spheroid.