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Description
Quote: pyTFM is a python package that allows you to analyze force generation and stresses in cells, cell colonies, and confluent cell layers growing on a 2-dimensional surface. This package implements the procedures of Traction Force Microscopy and Monolayer Stress Microscopy. In addition to the standard measures for stress and force generation, it also includes the line tension, a measure for the force transfer exclusively across cell-cell boundaries. pyTFM includes an addon for the image annotation tool clickpoints allowing you to quickly analyze and vizualize large datasets.
https://pytfm.readthedocs.io/en/latest/_images/mask_force_measures.png
Description

AnnotatorJ is a Fiji Plugin to ease annotation of images, particulrly useful for Deep Learning or to validate an alogorithm. Interestingly, it allows annotation for instance segmentation, semantic segmentation, or bounding box annotations. It includes toolssuch as active contours to ease these annotations.

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annotatorJ
Description

The tool exports rectangular regions, defined with the NDP.view 2 software (hammatsu) from the highest resolution version of the ndpi-images and saves them as tif-files.

Click the button and select the input folder. The input folder must contain pairs of ndpi and ndpa files. The regions will be exported to a subfolder of the input folder names zones.

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imagej toolset to export regions from ndpi and ndpa-files
Description

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default. Images need to be prepared such that they contain only one channel, but channel of ineterst (to be transformed with the same transformation) can be given as input, and Transform Virtual Stack plugin can be used as well.

F1000R Figure 1 DeepCLEM
Description

This tool allows to analyze morphological characteristics of complex roots. While for young roots the root system architecture can be analyzed automatically, this is often not possible for more developed roots. The tool is inspired by the Sholl analysis used in neuronal studies. The tool creates a binary mask and the Euclidean Distance Transform from the input image. It then allows to draw concentric circles around a base point and to extract measures on or within the circles. Instead of circles, which present the distance from the base point, horizontal lines can be used, which present the distance in the soil from the base-line. The following features are currently implemented:

  • The area of the root per distance/depth.
  • The number of border pixel per distance/depth, giving an idea of the surface in contact with the soil.
  • The maximum radius per distance/depth of a root, measured at the crossing points with the circles or lines.
  • The number of crossings of roots with the circles or lines.
  • The maximum distance to the left and the right from the vertical axis at crossing points with the circles or lines.
Concentric circles on the mask of a root, created by the Analyze Complex Roots Tool