CSBDeep, a toolbox for Content-aware Image Restoration (CARE) in Fiji

Description

Deep learning for fluorescence image restoration (denoising, deconvolution). Requires training on your data set but the procedure is described.

CARE

Neuroconductor

Description

Neuroconductor is an open-source platform for rapid testing and dissemination of reproducible computational imaging software, specialized in brain medical imaging (MRI, fMRI, DTI, etc...) but that could be used on a wider range of images. The goals of the project are to:

  • provide a centralized repository of R software dedicated to image analysis;
  • disseminate quickly software updates;
  • educate a large, diverse community of scientists using detailed tutorials and short courses;
  • ensure quality via automatic and manual quality controls; and
  • promote reproducibility of image data analysis.

 

Based on the programming language R, Neuroconductor starts with 68 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing.

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EBImage

Description

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

EBImage

ORION

Description

ORION: Online Reconstruction and functional Imaging Of Neurons: segmentation and tracing of neurons for reconstruction.

A project to develop tools that explore single neuron function via sophisticated image analysis. ORION software bridges advanced optical imaging and compartmental modeling of neuronal function by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function. The goal of this project is to develop a computational and experimental framework to allow real-time mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellularions) to neuronal structure, during the very limited duration of an acute experiment.

ORION_example_result

NeuronMetrics

Description

The invention comprises a software tool, NeuronMetrics, which functions as a set of modules that run in the open-source program ImageJ. NeuronMetrics features a novel method for estimating neural “branch number” (a measure of the axonal complexity) from two-dimensional images. In addition, the tool features a novel method for modeling neural structure in large “gaps” that result from image artifacts.

 

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Scipion

Description

Scipion is an image processing framework for obtaining 3D models of macromolecular complexes using Electron Microscopy (3DEM). It integrates several software packages and presents a unified interface for both biologists and developers. Scipion allows you to execute workflows combining different software tools, while taking care of formats and conversions. Additionally, all steps are tracked and can be reproduced later on.

http://scipion.cnb.csic.es/m/home/

Volocity - 3D Object Segmentation and Tracking

Description

In the commercial image analysis software "Volocity", automated measurement protocols can be constructed by dragging, dropping and configuring a sequence of individual "tasks".

By combining the "Find Objects" task with a subsequent "Track" task, 3D objects can be identified and followed over time. The initial "Find Objects" segmentation can be refined, e.g. using "Separate Touching Objects"; and tracking results in the form of "Measurement Items" can be viewed in tabular form, as a graph, etc.

spot detection and codistribution analysis

Description

WASH, Exo84, and cortactin spot detection and codistribution analysis To detect endosomes, an automatic Otsu threshold is applied to the Gaussian-filtered MT1-MMP–positive endosome image (= 1.5 pixels for the sample image). Statistics about each endosome are then saved, for example random positioning of spots can be compared to actual positioning. For each endosome, WASH and Exo84 (or WASH and cortactin) spots are searched for in a neighboring of x pixels in their respective channel. Their number and position are saved per endosome (**see the macro in Text file S2 downloadable from here**).

From the position of WASH and Exo84 (or WASH and cortactin) spots around each endosomes, each WASH spot is paired with its closest Exo84 (or cortactin) spot neighbor, optimized over all spots around this endosome.

This allowed measuring of the distribution of distance between WASH-Exo84 (or WASH-cortactin) spots (**for the co-distribution analysis, see matlab scripts in Zip file S3 downloadable).

endosomes and spot neighbors