abTEM is a pure Python code for running simulations of (scanning) transmission electron microscopy images using the multi -slice or PRISM algorithms. It is open-source, blazing fast, and extremely versatile and easy to extend.
AFT is an open-source workflow to quantify alignment of fibrillar features in bioimages. It allows for evaluation of alignment lengthscale and works with a number of different imaging modalities.
SR-Tesseler is an open-source interactive single molecule localisation microscopy clustering tool based on Voronoï tesselation, that allows for segmentation and quantification of localisation-based super-resolution microscopy data.
PlantSeg is a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline is tuned for images acquired with confocal and light sheet microscopy and can be used to segment both plant and animal tissues.
StatSTEM provides a user-friendly way of quantifying atomic-resolution scanning transmission electron microscopy (STEM) images by using parametric model-based fitting. In this manner, accurate and precise quantitative information can be extracted about the material under investigation.
Successor of SR-Tesseler and Coloc-Tesseler, Point Cloud Analyst is an interactive software platform that allows the quantification of multi-dimensional and multi-colour point clouds data, with a focus on single-molecule localisation microscopy data.
NanoJ is an open-source toolbox for super-resolution microscopy data analysis. It encompasses several essential processing steps; spatiotemporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics).
Superresolution Microscopy Analysis Platform (SMAP) is a freely available all-in-one analysis package for single molecule localisation microscopy data.
This is an open-source suite to mathematically model turnover rates in an intervention-free manner given the ability to quantify mRNA and protein expression from initiation to homeostasis.
MULTEM is a collection of routines written in C++ with CUDA to perform accurate and fast multi-slice simulations for different transmission electron microscopy experiments (such as HRTEM, STEM, ISTEM, ED, PED, CBED, ADF-TEM, ABF-HC, EFTEM and EELS).
StarDist is an AI-based tool for object detection with star-convex shapes in 2D and 3D. Intended for cell nuclei detection/segmentation in microscopy images, it can be used via the stand-alone Python software package, or with the provided plugins for other common image analysis software suites (e.g., Fiji, napari, QuPath).
Collection of Fiji macros to automate the boring stuff: batch process all files in a folder and save them by converting from .czi images (Bioformats) to .tif format; save all open images in .tif format in an output folder of choice; batch process all .tif files in a folder and save 3 channels separately; apply local contrast enhancement (CLAHE) to a Z-stack; batch segmentation of cell nuclei (or other blob-like objects) in fluorescence images by auto-thresholding.
pyxem is an open-source Python library for multi-dimensional diffraction microscopy. It has been primarily developed as a platform for hybrid diffraction-microscopy based on 4D scanning diffraction microscopy data in which a 2D diffraction pattern is recorded at every position in a 2D scan of a specimen.
