Resolving the process of Clathrin mediated endocytosis using Correlative Light & Electron Microscopy (CLEM)

Description

Multimodal image registration based on manual selection of matching pairs of landmarks. This image registration workflow is based
on MATLAB’s image processing toolbox using the identification of sites of clathrin-mediated endocytosis by correlative light electron microscopy (CLEM) as an example.

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Motility analysis with mean-square displacement

Description

Tracking tools, such as TrackMate, produce tracks and their role stops there. However, tracks are just an intermediate data structure in the workflow. Their subsequent analysis produces the numbers upon which scientific conclusions are made. The track analysis is most often specific to the scientific question to be addressed, and therefore tracking tools remain generic and seldom include specialized analysis modules. Another toolset is required for track analysis; this workflow focuses on using MATLAB.

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The NEMO dots assembly: Single-particle tracking and analysis

Description

This workflow presents single-particle tracking in Fiji using Track-Mate, and track motility analysis in MATLAB using @msdanalyzer. 

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Daybook2

Description

Daybook 2 is the analysis software linked to argoligth slides. It tests the performance of microscopes on various levels: illumination homogeneity, field distortion, lateral resolving power, stage drift, chromatic aberrations, intensity response... It works with various file formats but requires the use of an argolight test slide. 

AssayScope

Description

AssayScope is an intuitive application dedicated to large scale image processing and data analysis. It is meant for histology, cell culture (2D, 3D, 2D+t) and phenotypic analysis. 

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IntelliJ IDEA

Description

IDE for JVM

Every aspect of IntelliJ IDEA is specifically designed to maximize developer productivity. Together, the powerful static code analysis and ergonomic design make development not only productive but also an enjoyable experience.

ZEN Intellesis Trainable Segmentation

Description

Perform Advanced Image Segmentation and Processing across Microscopy Methods
 

Overcome the bottleneck of segmenting your Materials Science images and use ZEISS ZEN Intellesis, a module of the digital imaging software ZEISS ZEN.
Independent of the microscope you used to acquire your image data, the algorithm of ZEN Intellesis will provide you with a model for automated segmentation after training. Reuse the model on the same kind of data and beneft from consistent and repeatable segmentation, not influenced by the operator. 
ZEN Intellesis offers a straightforward, ease-to-use workflow that enables every microscope user to perform advanced segmentation tasks rapidly.

Highlights

  • Simple User Interface for Labelling and Training
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets
  • Use powerful machine learning algorithms for pixel-based classifcation
  • Real Multi-Channel Feature Extraction
  • Engineered Feature Set and Deep Feature Extraction on GPU
  • IP-Function for creating masks an OAD-enabled for advanced automation
  • Powered by ZEN and Python3 using Anaconda Python Distribution
  • Just label objects, train your model and segment your images – there is no need for expert image analysis skills
  • Segment any kind of image data in 2D or 3D. Use data from light, electron, ion or x-ray microscopy, or your mobile phone
  • Speed up your segmentation task by built-in parallelization and GPU (graphics processing unit) acceleration
  • Increase tolerance to low signal-to-noise and artifact-ridden data
  • Seamless integration in ZEN framework and image analysis wizard
  • Data agnostic
  • Compatibility with 2D, 3D and up to 6D datasets
  • Export of multi-channel or labeled images
  • Exchange and sharing of models
  • GPU computing
  • Large data handling
  • Common and well-established machine learning algorithms
  • SW Trial License available

3D confocal noise simulator

Description

This Matlab code simulates the noise of the confocal laser scanning microscope depending on the depth in the image stack (serial sections). Using the stack of binary images, it applies different levels of noise in the signal and background parts of the images to simulate confocal images. This is useful for generating "virtual ground truth" images with known values of sample rotation and distortion.