ROI 1-click tools for ImageJ/Fiji

Description

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 

CAREless

Description

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.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

need a thumbnail

ImageM

Description

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

need a thumbnail

Yapic

Description

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 

has function
need a thumbnail

Piximi

Description

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

has topic
has function
need a thumbnail

StarDist - ImageJ

Description

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.

Stardist

IR tools

Description

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

need a thumbnail

MorphoNet Python API

Description

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

Description

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. 

By handling a broad range of natural or simulated morphological data, it fills a gap which has until now limited the quantitative understanding of morphodynamics and its genetic underpinnings by contributing to the creation of ever-growing morphological atlases.