Image segmentation is (one of) the (few) concept(s) on the border between Image (pre)processing (Image->Image) and Image analysis (Image->Data).

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

QCAnet

Description

Quantitative Criterion Acquisition Network (QCA Net) performs instance segmentation of 3D fluorescence microscopic images. QCA Net consists of Nuclear Segmentation Network (NSN) that learned nuclear segmentation task and Nuclear Detection Network (NDN) that learned nuclear identification task. QCA Net performs instance segmentation of the time-series 3D fluorescence microscopic images at each time point, and the quantitative criteria for mouse development are extracted from the acquired time-series segmentation image. The detailed information on this program is described in our manuscript posted on bioRxiv.

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ImagePy

Description

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.

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. 

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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NiftyNet

Description

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

  • Get started with established pre-trained networks using built-in tools;
  • Adapt existing networks to your imaging data;
  • Quickly build new solutions to your own image analysis problems.

HyphaTracker

Description
HyphaTrackerWorkflow
HyphaTracker Workflow

HyphaTracker propose a workflow for time-resolved analysis of conidia germination. Each part of this workflow can also be used independnatly , as a toolbox. It has been tested on bright-field microscopic images of conidial germination. Its purpose is mainly to identify the germlings and to remove crossing hyphae, and measure the dynamics of their growth.

hyphatracker

LimeSeg

Description

LimeSeg: A coarsed-grained lipid membrane simulation for 3D image segmentation

Download instruction:

There is no download but you can easily install this plugin via ImageJ update site. If you reallu need to download the jar file, access the file in the update site repository (Link)

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Tensorflow

Description

"An open source machine learning framework for everyone "

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

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TensorFlow