Contents

EPySeg is a package for segmenting 2D epithelial tissues. EPySeg also ships with a graphical user interface that allows for building, training and running deep learning models.

Training can be done with or without data augmentation (2D-xy and 3D-xyz data augmentation are supported). EPySeg relies on the segmentation_models library. EPySeg source code is available here. Cloud version available here.

VTK is an open-source software system for image processing, 3D graphics, volume rendering and visualization. VTK includes many advanced algorithms (e.g., surface reconstruction, implicit modeling, decimation) and rendering techniques (e.g., hardware-accelerated volume rendering, LOD control).

VTK is used by academicians for teaching and research; by government research institutions such as Los Alamos National Lab in the US or CINECA in Italy; and by many commercial firms who use VTK to build or extend products.

The BioVoxxel Toolbox is a suite which contains plugins and some macros dealing with image filtering, image segmentation and binary image processing and analysis. The following plugins are hosted here:

Fast4DReg is a Fiji macro for drift correction for 2D and 3D video and is able to correct drift in all x-, y- and/or z-directions. Fast4DReg creates intensity projections along both axes and estimates their drift using cross-correlation based drift correction, and then translates the video frame by frame. Additionally, Fast4DReg can be used for alignment multi-channel 2D or 3D images which is particularly useful for instruments that suffer from a misalignment of channels.

Crop 2D/3D images with arbitrary box size and orientation(s).

The RotCrop Plugins for ImageJ allow to perform rotated crop of 2D/3D images. In fully manual versions, the crop can be defined based on crop dimensions, crop center, and rotation angle(s) of the crop frame (in 3D, three angles are necessary).

Spine Analyzer allows to semi-automatically segment dendritic spines in 3D+t images and to measure their volumes and the intensities of the signal within in different channels over time.

A collection of Image Processing and Analysis (IPA) functions used at the Facility for Advanced Imaging and Microscopy (FAIM).

DeXtrusion is a machine learning based python pipeline to detect cell extrusions in epithelial tissues movies. It can also detect cell divisions and SOPs, and can easily be trained to detect other dynamic events.

BIIGLE is a web-based software for image and video annotation that enables collaborative research on large datasets. It offers tools for manual and computer-assisted annotation, quality control and the collaboration on custom taxonomies to describe objects. BIIGLE is freely available and can be installed in cloud environments, a local network or on mobile platforms during research expeditions. The public instance on biigle.de is free for non-commercial use.

HistoMetrix is an advanced histology analysis software designed to simplify image processing and analysis for biologists and pathologists. Powered by the most advanced deep learning technology, HistoMetrix enables you to effortlessly uncover valuable insights and visualize results without the need for extensive technical expertise. Let’s explore the key features that make HistoMetrix the ultimate solution for histology analysis.

TissUUmaps is a browser-based tool for fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used as a web service or locally in your computer, and allows users to share regions of interest and local statistics.

The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls. This website provides ressources such as a tool to select the best metric, as well as tutorials about the way to use and interpret metrics in image analysis.

While a quickly retrained cellpose network (only on xy slices, no need to train on xz or yz slices) is giving good results in 2D, the anisotropy of the SIM image prevents its usage in 3D. Here the workflow consists in applying 2D cellpose segmentation and then using the CellStich libraries to optimize the 3D labelling of objects from the 2D independant labels.

Here the provided notebook is fully compatible with Google Collab and can be run by uploading your own images to your gdrive. A model is provided to be replaced by your own (create by CellPose 2.0)

CellStich proposes a set of tools for 3D segmentation from 2D segmentation: it reassembles 2D labels obtained from cell in slices in unique 3D labels across slices. It isparticularly robust to anisotropy, and is the ideal companion to cellpose 2D models or other 2D deep learning based models. One could also think about using it for cell tracking by overlap (using time as a third dimension).

Hi,

I am getting backend error when using full text search, for example: https://biii.eu/search?search_api_fulltext=imagej

error:
-----------------------------------------------

Error 503 Backend fetch failed

Backend fetch failed

Guru Meditation:

XID: 631819717

Varnish cache server
-------------------------------------------

Proxywave is software designed to assist users in creating and managing their own mobile proxies using wifi dongles. With Proxywave, users can easily create mobile proxy without any technical knowledge or experience. This platform provides a cost-effective solution for users who want to create their own mobile proxies and start their own proxy businesses. With the ability to create your own mobile proxy, Proxywave offers users a simple and easy-to-assemble solution for their proxy needs.

Image segmentation and object detection performance measures

The goal of this package is to provide easy-to-use tools for evaluation of the performance of segmentation methods in biomedical image analysis and beyond, and to fasciliate the comparison of different methods by providing standardized implementations. This package currently only supports 2-D image data.

SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.

MATLAB app to characterize nanoparticles imaged with super-resolution microscopy. nanoFeatures will read text and csv files from the NIKON and ONI microscopes and from the ThunderSTORM Fiji plugin, then cluster the localizations and filter by size and sphericity and finally output nanoparticle features like size, aspect ratio, and number of localizations per cluster (total and for each channel).

These are commands that create or process binary (black and white) images. Typical morphological operations/functions can be found here.