Bioimage informatics

Bioimage informatics

Synonyms
Bioimage analysis
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

The napari-pyclesperanto-assistant is a yet experimental napari plugin for building GPU-accelerated image processing workflows targeting life-sciences and bio-image analysis. It is part of the clEsperanto project. It uses pyclesperanto and pyopencl as backend for processing images.

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Description

Phindr3D is a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) high content screening image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and data visualization.

Please see our GitHub page and the original publication for details.

Description
Quote: >LaRoME = Label + Region Of Interest + Measure >Label image (aka Count Masks): An image in which pixels of an object have all the same value. Each object has a unique value. >Measurement image: An image in which pixels of an object have all the same value, corresponding to a measurement (Area, Angle, Mean...)
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Data Science in Cell Imaging (DSCI) course material

Submitted by assafzar on Thu, 12/31/2020 - 10:08

Graduate course at the department of Software and Information Systems Engineering at Ben-Gurion University of the Negev.

The recent explosion in high-content, dynamic and multidimensional imaging data is transforming cell imaging into a “Data Science” field. This course will review the state-of-the-art in visualizing, processing, integrating and mining massive cell image data sets, deciphering complex patterns and turning them into new biological insight. It will include a mix of approaches in computer science, machine learning and computer vision applied to bio-imaging data.