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BaSiC is a software tool for Background and Shading correction of Optical Microscopy Images. It implements an image correction method based on low-rank and sparse decomposition to solve both shading in space and background variation in time. It can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC is available as a Fiji/ImageJ plugin.

Voxelytic-Align is a commercial software provided as a service on cloud targetted to electron microscopy reconstruction (alignement, artifact correction...)

Hot-Knife is a library specifically designed for FIB-SEM data and thick sections, which includes code for flattening, deformable alignment with features and a more robust kind of block-matching, and some visualization, manual correction, and import and export tools (n5).

A collection of Java tools and HTTP services (APIs) for rendering transformed image tiles that includes:

Removal of heterogeneous background from image data of single-molecule localization microscopy, using extreme value-based emitter recovery (EVER).

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EVER requires no manual adjustment of parameters and has been implemented as an easy-to-use ImageJ plugin that can immediately enhance the quality of reconstructed super-resolution images. This method is validated as an efficient way for robust nanoscale imaging of samples with heterogeneous background fluorescence, such as thicker tissue and cells.

The napari-pyclesperanto-assistant is a yet experimental napari plugin for building GPU-accelerated image processing workflows targeting life-sciences and bio-image analysis. It is part of the clEsperanto project. It uses pyclesperanto and pyopencl as backend for processing images.

Thie lecture is for Python beginners who want to dive into image processing with Python. It specifically aims for students and scientists working with microscopy images in the life sciences. We start with python basics, dive into descriptive statistics for working with measurements and matplotlib for plotting results.

These slides give an introduction to user interfacre customization in ImageJ using ImageJ Macro and to ImageJ Macro Markdown.

Big thanks to Jerome Mutterer (IBMP) and Nicolas De Francesco (IMBICE) who shared material openly I reused when making these slides.

Supplementary material is available as well under this doi: 10.5281/zenodo.4561714

Talk about sharing and licensing materials such as [raw] data, manuscripts, code, slides, ... at the EMBO Course on Advanced Methods for Bio-image Analysis 2021 virtually in Heidelberg

These slides were shown in the "On-the-fly image processing session with Python and napari" session by Robert Haase of the Smart Microscopy Workshop at the Center for Cellular imaging at the University of Gothenbourg in May 2021.

AnnotatorJ is a Fiji Plugin to ease annotation of images, particulrly useful for Deep Learning or to validate an alogorithm. Interestingly, it allows annotation for instance segmentation, semantic segmentation, or bounding box annotations. It includes toolssuch as active contours to ease these annotations.

The tool exports rectangular regions, defined with the NDP.view 2 software (hammatsu) from the highest resolution version of the ndpi-images and saves them as tif-files.

Click the button and select the input folder. The input folder must contain pairs of ndpi and ndpa files. The regions will be exported to a subfolder of the input folder names zones.

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

This lesson shows how to use Python and skimage to do basic image processing.

This is a resource for image analysis training material, with a focus on research in the life sciences.

Currently, this resource is mainly meant to serve image analysis trainers, helping them to design courses. However, we might add more text (or videos) to the material such that it could also be used by students for self-directed study.

The datasets consist of ANNOTATED 2D and 3D time-lapse video sequences of fluorescent counterstained nuclei or cells moving on top or immersed in a substrate, along with 2D Bright Field, Phase Contrast, and Differential Interference Contrast (DIC) microscopy videos of cells moving on a flat substrate. The videos cover a wide range of cell types and quality (spatial and temporal resolution, noise levels etc.) In addition, we provide 2D and 3D videos of synthetic fluorescently stained cells and nuclei of different shapes and motion patterns.

Phindr3D is a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) high content screening image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and data visualization.

Please see our GitHub page and the original publication for details.

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default.