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

pystackreg

Description

Python/C++ port of the ImageJ extension TurboReg/StackReg written by Philippe Thevenaz/EPFL.

A python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame.

need a thumbnail

cvMatch_Template

Description

It implements the template matching function from the OpenCV library. The java interface of OpenCV was done through the javacv library. It is quite similar as the existing template matching plugin but runs much faster and users could choose among six matching methods: 

1.Squared difference

2.Normalized squared difference

3.Cross-correlation

4.Normalized cross-correlation

5.Correlation coefficient

6.Normalized correlation coefficient

The detailed algorithms could be found here.

The cvMatch_Template will search a specific object (image pattern) over an image of interest by the user-specified method. 

Drishti

Description

Drishti (from Sanskrit  word for "vision" or "insight") is a multi-platform, open-source volume-exploration and presentation tool. Written for visualizing tomography data, electron-microscopy data and the like.

Drishti

Z-spacing correction for Fiji

Description

Estimate the positions and spacing between sections (or at local points) of three dimensional image data. This method may be applied to any imaging modality that acquires 3-dimensional data as a stack of 2-dimensional sections. We provide plugins for both Fiji and TrakEM2.

has function

Isotropic Super-Resolution for EM

Description

Super-resolve anisotropic EM data along low-res axis with deep learning.

 

has function

McLuigi

Description

Multicut workflow for large connectomics data. Using luigi for pipelining and caching processing steps. Most of the computations are done out-of-core using hdf5 as backend and implementations from nifty

SuRVoS

Description

SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.

User interface of SuRVoS showing example annotation on soft x-ray tomography data

Bigwarp

Description

Bigwarp is a tool for manual, interactive, landmark-based deformable image alignment. It uses the BigDataViewer for visualization and navigation, and uses a Thin Plate Spline implemented in Java to build a deformation from point correspondences.

need a thumbnail

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/