Synonyms
Phase imaging
Quantitative phase contrast imaging

DeepCell

Description

 

DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )

deepcell

Mask-RCNN

Description

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

Holovibes

Description

Holovibes is a free software dedicated to the calculation of holograms in real-time. Input interferogram data can be grabbed from a digital camera or loaded from files recorded beforehand. Massive amounts of data can be handled robustly at high throughput, saved to disk, and visualized in real-time without any risk of frame dropping thanks to the use of several configurable input and output memory buffers.

Main features

Image acquisition from several digital cameras or from data files
Choice of hologram rendering method
Blazing-fast hologram rendering
Real-time computation of spectrograms
Hologram autofocus
Image and video post-processing
High throughput saving to disc of massive datasets
Batch recording and communication with remote instruments via GPIB

Requirements

A PC with at least 8 GB of RAM
Microsoft Windows 7/10 64-bit operating system
A NVidia graphics card (GeForce GTX 700+ series)
NVidia CUDA 9
A supported digital camera, or raw interferogram files

Use case examples

Holographic microscopy
Holographic OCT
Holographic vibrometry
Holographic angiography
Holographic plethysmography

need a thumbnail

video tutorial on 3D vessel segmentation of synchrotron phase contrast tomography

Bioimage Analyst
Developer

In this tutorial video, a coronary arterial tree is used as the demo example to show in detail how the semi-automatic segmentation workflow, Carving from the open-source image analysis software ilastik, can be used. Tips on how and why a preprocessing is done, as well as parameter settings are provided.

PHANTAST for FIJI

Description

 

The phase contrast microscopy segmentation toolbox (PHANTAST) is a collection of open-source algorithms and tools for the processing of phase contrast microscopy (PCM) images. It was developed at University College London's department of Biochemical Engineering and CoMPLEX.

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iTrack4U

Description

iTrack4U is a Java-based software using ImageJ and jMathPlot libraries, which aims at automatically tracking cells recorded in phase-contrast microscopy. It includes all tools from image files preprocessing, tracking to data extraction and visualization. 

 

Please cite Cordeliéres et. al. (2013) when using this software package!

iTrack4U

Cell segmentation in phase contrast images

Description

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

initialisation

MATLAB CellDetector

Description

CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets. The installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.

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Cell segmentation and quantification with CellX

Description

CellX is an open-source software package of workflow template for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images with distinguishable cell boundary.

Installation and step-by-step usage details are described in Mayer et al (2013). 

After users provide a few annotations of cell sizes and cell boundary profiles, it tries to match boundary profile pattern on cells thus provide segmentation and further tracking. It works the best on cells without extreme shapes and with a rather homogeneous boundary pattern. It may not work well on images with cells of sizes only a few pixels. Its output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.