Contents

A Python toolbox for registering / fusing / stitching large multi-view / multi-positioning image datasets in 2-3D.

FishFeats: napari plugin to perform quantification of multimodal labeling at the single-cell level in 3D tissues.

FishFeats allows to perform together in the same pipeline several analysis to quantify epithelia cells in 3D tissue, analysing cell morphology, nuclei, immuno-staining or RNA expression. The plugin allows flexibility to let the user choose the relevant step for a specific biological question.

In the first part the workshop takes a quick tour of the different tools available in QuPath. In the second part the tools are used to analyze fluorescence and IHC images. The aim of the workshop is to help the participants getting started to use QuPath for their image analysis projects, to understands the methods applied and the results obtained and to avoid possible pitfalls.

Program

1. Project creation

2.

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.

This toolkit extracts Spherical Textures: Angular projections of 2D or 3D image objects with subsequent spherical harmonics analysis.

The Allen Mouse Brain Common Coordinate Framework (CCF) is a 3D reference atlas for the mouse brain which can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D. The spatial template is constructed as a population average of 1,675 young adult mouse brains imaged using serial two photon tomography. 

Trace ridges combines filtering, Watershed transforms, Edge detection and mathematical morphology to trace ridges in an image with fibre-like structures. 
Publication provides objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based).

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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.

napari-lattice is a napari plugin designed for the analysis and visualization of Lattice Lightsheet Microscopy (LLSM) and Oblique Plane Microscopy (OPM) data, particularly focusing on data acquired from Zeiss Lattice Lightsheet systems. Also available as lls-core - a command line version of the same tool which does not require napari.

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. 

AnyLabeling is Effortless AI-assisted data labeling tool with AI support from Segment Anything and YOLO models!

AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling

Installation

Standalone (executable)

The executable file links are provided in Assets section here

PlantSeg is a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline uses a two stages segmentation strategy (Neural Network + Segmentation). The pipeline is tuned for plant cell tissue acquired with confocal and light sheet microscopy. Pre-trained models are provided.