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This macro toolset offers additional click tools for the rapid annotations of ROI in ImageJ/Fiji.

The ROI 1-click tools can be setup with a predefined shape, and custom actions to perform upon click (Add to ROI Manager, Run Measure, Go to next slice, run a macro command...)

To install in Fiji, just activate the ROI 1-click tools 

SPHIRE is a new software suite designed for easy access to cryo electron microscopy with the clear goal of quality assessment and result reproducibility by statistical resampling. While being well suited for cryo-EM novices, experienced users will find comfort in the accessibility of almost every possible variable in advanced option tabs and the transparent, easily customizable Python-based framework for non-standard processing pipelines.

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

A forum for discussions on image analysis software, hardware, events etc.

ImageM integrates into a GUI several algorithms for interactive image processing and analysis. Interface is largely inspired from the open source software "ImageJ".

This is a database of pretrained deep Learning models. 

Yet another pixel classifier Yapic is a deep learning tool to :

train your own filter to enhance the structure of your choice 

train multiple filter at once 

it is based on the u-net convolutional filter . 

To train it : annotation can come from example from Ilastik software , tif labelled files can be transferred to yapic. 

Training takes about hours to days , prediction takes seconds once trained .

It can be ran from command line .

note that only 10 to 20 images with sparse labeling are required for efficient training 

LobSTEr : handle terabyte of data without concerns about the memory available .

75 workflows available 

no programming , matlab based 

Web based application for deep learning cell classifier . Aim to replace cellprofiler analyst and advanced cell classifier . Under construction. 

This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.

Deep learning based web app upload your data and get segmentation

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.

A MATLAB Package of Iterative Regularization Methods and Test Problems for Linear Inverse Problems (for Matlab Version 9.3 or later).

The Morphonet Python API provide an easy interface to interact directly with the MorphoNet server. Very useful to upload, download your dataset and superimpose on it any quantitative and quantitative informations.

MorphoNet is a novel concept of web-based morphodynamic browser to visualise and interact with complex datasets, with applications in research and teaching. 

MorphoNet offers a comprehensive palette of interactions to explore the structure, dynamics and variability of biological shapes and its connection to genetic expressions. 

VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

Vaa3d BJUT Fast Marching Spanning Tree algorithm dockerised workflow for BIAFLOWS

Blood vessels tracing in 3D image from 3D Gaussian blurring (user defined radius), local thresholding (user defined radius and offset) and 3D skeletonization. Dockerized version for BIAFLOWS,

Blood vessels tracing in 3D image from Tubeness filtering (user defined scale), 3D opening (radius set to 2), thresholding (user defined level) and 3D skeletonization.