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 

SPHIRE

Description

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. In a visually appealing and easy-to-use graphical user interface (GUI) the user will find an array of programs which will guide through the complete process of high-resolution cryo-EM. This begins with movie frame alignments (movie), CTF estimation of raw electron micrographs (cter) and picking/stack creation (window) and continues with reproducible 2-D classification (isac), reproducible initial model generation (viper), automatic gold-standard 3-D refinement (meridien), local resolution estimation and filtering (localres), up to the 3-D sorting of different conformational states based on the statistical 3-D variability of the data (sort3D).

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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.

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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".

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Piximi

Description

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

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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).

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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.