Python

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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.

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.

QuantiFish is a quantification program intended for measuring fluorescence in images of zebrafish, although use with images of other specimens is possible. This package is geared towards analysis of fluorescent infection models. The software is designed to automate processing of images of single fish, and outputs results as a .csv file. Alongside measures of total fluorescence above a threshold, this package also introduces several measures for dissemination and distribution of fluorescence throughout the specimen.

The library contains several helper functions to generate MoBIE project folders and add data to it.  Itis a python library to generate data in the MoBIE data storage layout. 

For further information, look to http://biii.eu/mobie-fiji-viewer

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.