Collection

The Plant Computer Vision (PlantCV) software package, is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. 

PlantCV v2 is the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

PlantCV is composed of modular functions in order to be applicable to a variety of plant types and imaging systems. PlantCV currently supports the analysis of standard RGB color images (aka "VIS"), standard grayscale images (e.g. near-infrared, "NIR"), thermal infrared images, grayscale images from chlorophyll fluorescence imaging systems ("PSII"), and hyperspectral ("ENVI") images. 

BraiAn is an open-source suite of tools designed to simplify signal quantification, analysis and visualization of large datasets typically obtained in whole-brain imaging experiments, following registration to an atlas. 

The package consists of two separate modules.

1. BrainAnDetect: A QuPath extension for multi-channel cell segmentation across large and variable datasets. It leverages QuPath's built in algorithms for cell detection, and features additional options for refining signal quantification, including machine-learning-based object classification, region-specific cell segmentation, multiple marker co-expression analysis, and an interface for selective exclusion of damaged tissue portions.
2. BraiAnalyse: A modular Python library for the easy navigation, visualization, and analysis of whole-brain quantification outputs.

Ultrack is a versatile and scalable cell tracking method designed to address the challenges of tracking cells across 2D, 3D, and multichannel timelapse recordings, especially in complex and crowded tissues where segmentation is often ambiguous. By evaluating multiple candidate segmentations and employing temporal consistency, Ultrack ensures robust performance under segmentation uncertainty. Ultrack's methodology is explained here.

(from https://github.com/royerlab/ultrack)

Description from Github page:

A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data.
Provides a GUI for neural network models including Segment Anything Model (SAM), YeaZ, cellpose, StarDist, YeastMate, omnipose, delta, DeepSea.

MetaXpress or in full name "MetaXpress® High-Content Image Acquisition and Analysis Software" is a commercially available closed source software for high-content analysis from Molecular Devices, LLC.. The program is a kind of visually guided workflow programming environment. There is a programming module called CME (custom module editor) which lets one setup integrated workflows for bioimage analysis with visual feedback. It is designed for high-throughput in connection with a included database which stores the experimental data. 

It has several toolboxes for semiautomated processing of various tasks:

3D Analysis (requires Custom Module Editor), Curve fitting, Transmitted light segmentation (requires Custom Module Editors), Angiogenesis tube formation, Cell cycle, Cell health, Cell scoring , Count nuclei, Granularity, Live/dead , Mitotic index, Micronuclei , Monopole detection, Multi-Wavelength cell scoring, Multi-wavelength translocation, Neurite outgrowth , Transfluor® Assay, Translocation* (includes Translocation-Enhanced*) , Transfluor HT Assay , Nuclear translocation HAT, Cell proliferation HT

After the workflow is setup it is possible to apply it automatically to a stack of stored images. The derived data from those analyses is stored in the metaxpress database and can be exported from there.

The use of each toolbox requires a separate license.