library

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

From the author summary: "We introduce a novel method to extract quantitative data from microscopy images by precisely measuring the distribution of intensities within objects in both 3D and 2D. This method is easily accessible through the object classification workflow of ilastik, provided the original image is segmented into separate objects. The method is specifically designed to analyze the convex region in objects, focusing on the variation in fluorescence intensity caused by differences in their shapes or patterns."

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.

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. 

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. 

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