multi-channel

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.

Quote:

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.

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.

napari-lattice allows users to deskew and deconlolve lattice light sheet, or any oblique plane microscopy, data. To speed processing, users can provide ROIs to be cropped and processed separately.  This significantly speeds up processing time and allows many options for parallelisation. 

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

Install from source

git clone https://github.com/vietanhdev/anylabeling
cd anylabeling
pip install .

Install from PyPI

pip install anylabeling

With GPU support:

pip install anylabeling-gpu

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.