Cell segmentation

ASTEC stands for Adaptive Segmentation and Tracking of Embryonic Cells. It proposes a full workflow for time lapse light sheet imaging analysis, including drift/motion compensation before the segmentation itself, and the capacity to correct for it.  It was used to process 3D+t movies acquired by the MuViSPIM light-sheet microscope in particular.

ClearMap is a toolbox for the analysis and registration of volumetric data from cleared tissues.

It was initially developed to map brain activity at cellular resolution in whole mouse brains using immediate early gene expression. It has since then been extended as a tool for the qunatification of whole mouse brain vascualtur networks at capilary resolution.

It is composed of sevral specialized modules or scripts: tubemap, cellmap, WobblyStitcher.

ClearMap has been designed to analyze O(TB) 3d datasets obtained via light sheet microscopy from iDISCO+ cleared tissue samples immunolabeled for proteins. The ClearMap tools may also be useful for data obtained with other types of microscopes, types of markers, clearing techniques, as well as other species, organs, or samples.

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

The tool allows to measure the area of the invading spheroïd in a 3D cell invasion assay. It can also count and measure the area of the nuclei within the spheroïd.

Histology Topography Cytometry Analysis Toolbox (histoCAT) is a package to visualize and analyse multiplexed image cytometry data interactively. It can also export data in.fcs data for further analysis using  a specialized cytometry sofwtare such as Flowjo. 

It can be run as a compiled standalone or from matlab.

BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. 

• Integrated with Fiji, ilastik, ImJoy and more
• Try model instantly with BioEngine
• Contribute your models via Github

This is a database of pretrained deep Learning models. 

LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images, i.e. terabytes of data, exceeding workstation main memory.

• 75 workflows available 
• no programming, with GUI
• matlab based 

This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.

Summary

Deep learning-based segmentation of cells, both fluorescence, and bright-field images ("a generalist algorithm for cellular segmentation"). The tool can be used either online or local or via notebooks (e.g. ZeroCostDL4Mic).

How to use it

cellpose can be used online via ready-to-use Jyupyter notebooks with very good documentation. These notebooks are listed here.

Local Installation

The general local installation procedure can be found here.

... Installing to Silicon Mac (M1 processor) needs some tricks, and as of October 2021, the following sequence of commands works. numba should be conda-installed before pip-installing the cellpose.

conda create --name cellpose python=3.8
conda activate cellpose
conda install numba
git clone https://github.com/MouseLand/cellpose.git
cd cellpose
pip install -e .

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.

Training developper constructs and upload trained model, and made them available to users.

Models are available in a repository here.

It is macro recordable. It is advised to use DeepImageJ on a computer with GPU (CPU will likely be 20x slower)

VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.

U-Net segmentation as presented in Reference Publication. The model predicts three classes: background, edge and foreground. The model was trained with Kaggle Data Science Bowl (DSB) 2018 training set.

Nuclei Segmentation using Deep Learning for individual cell analysis (DeepCell).

OligoMacro Toolset, is an ImageJ macro-toolset aimed at isolating oligodendrocytes from wide-field images, tracking isolated cells, characterizing processes morphology along time, outputting numerical data and plotting them. It takes benefit of ImageJ built-in functions to process images and extract data, and relies on the R software in order to generate graphs.

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 )

There are many methods in bio-imaging that can be parametrized. This gives more flexibility
to the user as long as tools provide easy support for tuning parameters. On the other hand, the
datasets of interest constantly grow which creates the need to process them in bulk. Again,
this requires proper tool support, if biologist is going to be able to organize such bulk
processing in an ad-hoc manner without the help of a programmer. Finally, new image
analysis algorithms are being constantly created and updated. Yet, lots of work is necessary to
extend a prototype implementation into product for the users. Therefore, there is a growing
need for software with a graphical user interface (GUI) that makes the process of image
analysis easier to perform and at the same time allows for high throughput analysis of raw
data using batch processing and novel algorithms. Main program in this area are written in
Java, but Python grow in bioinformatics and will be nice to allow easy wrap algorithm written
in this language.
Here we present PartSeg, a comprehensive software package implementing several image
processing algorithms that can be used for analysis of microscopic 3D images. Its user
interface has been crafted to speed up workflow of processing datasets in bulk and to allow
for easy modification of algorithm’s parameters. In PartSeg we also include the first public
implementation of Multi-scale Opening algorithm descibed in [1]. PartSeg allows for
segmentation in 3D based on finding connected components. The segmentation results can be
corrected manually to adjust for high noise in the data. Then, it is possible to calculate some
standard statistics like volume, mass, diameter and their user-defined combinations for the
results of the segmentation. Finally, it is possible to superimpose segmented structures using
weighted PCA method. Conclusions: PartSeg is a comprehensive and flexible software
dedicated to help biologists in processing, segmentation, visualization and the analysis of the
large microscopic 3D image data. PartSeg provides well established algorithms in an easy-touse,
intuitive, user-friendly toolbox without sacrificing their power and flexibility.

Examples include Chromosome territory analysis.

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. 

"The Microscope Image Analysis Toolbox MiToBo is an extension for the widely used image processing application ImageJ and its new release ImageJ 2.0.
MiToBo ships with a set of operators ready to be used as plugins in ImageJ. They focus on the analysis of biomedical images acquired by various types of microscopes."

Nessys: Nuclear Envelope Segmentation System

Nessys is a software written in Java for the automated identification of cell nuclei in biological images (3D + time). It is designed to perform well in complex samples, i.e when cells are particularly crowded and heterogeneous such as in embryos or in 3D cell cultures. Nessys is also fast and will work on large images which do not fit in memory.

Nessys also offers an interactive user interface for the curation and validation of segmentation results. Think of this as a 3D painter / editor. This editor can also be used to generate manually segmented images to use as ground truth for testing the accuracy of the automated segmentation method.

Finally Nessys, contains a utility for assessing the accuracy of the automated segmentation method. It works by comparing the result of the automated method to a manually generated ground truth. This utility will provide two types of output: a table with a number of metrics about the accuracy and an image representing a map of the mismatch between the result of the automated method and the ground truth.