Cytomine is a web-based open source solution, aiming to empower whole-slide image processing, & analysis with machine learning algorithms. It's built to ease collaboration among researchers.
Cytomine is built by a group of researchers from Montefiore Institute (University of Liège, Belgium) who are developing machine learning algorithms and big data software modules aiming to provide an open-source solution for processing very large imaging data.
Unlike Orbit which we introduced in this article, Cytomine is considered lightweight, web-based, easy to install, & does not require heavy-duty hardware requirements like an orbit. It can be installed on a web server, a laptop or a desktop.
In short, Cytomin is a self-hosted cloud-based whole-slide image viewer, with multi-user, multi-projects options, empowered by machine learning algorithms, So as a web-based WSI viewer, it improves pathologists workflow, as it provides out-of-box collaboration tool, speed-up image analysis, improves the quality, and reduce the errors.
The project is meant for biologists, biomedical researchers, computer scientists, software engineers, & pathologists.
Before we dive into how Cytomine works, let's start by talking about the major elements in digital pathology: Whole slide scanners, and Whole slide image, but what is Digital Pathology?
Digital Pathology is an image-based information environment that transforms the classical traditional physical glass slides or physical samples into virtual slides using Whole-image scanners. These scanners are scanning the slides to generate a detailed high-resolution large image in gigapixel.
Unlike images, we capture with our phones, or cameras, Whole-slide images size is large, as used scanner (Whole-slide image scanners) that supports capturing resolutions of 0.5 microns/pixel, allowing high magnification. Opening virtual slides on the traditional image viewer programs are quite a challenge as they are not equipped large files and most of them do not support virtual slide image formats, the non-standard metadata, & the file compression used by Whole-image scanners.
Cytomine has been used in several types of research which shows how can it be used, it can also be used for education (Histology, Pathology), you may check the research, & publication section at Cytomine website. Cytomine has ongoing researches which demonstrate it's value to open source digital pathology community, here is some of them:
Cytomine developers used several tech stacks to build it, They used web-technologies to create the web-based client and make it compatible with the modern web browsers like Google Chrome, Mozilla Firefox, & Safari. They used Java technologies which makes it cross-platform (Linux, macOS, Windows). Here is a list of the tech stack used:
The project has detailed installation instructions for Linux, & macOS, A demo account can be requested from the project maintainers. It has detailed documentation for developers, and data scientists for scripting, data mining, available algorithms, REST-API, & how to build and add extensions.
Cytomine is released as an open-source project under Apache 2.0 license
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A physician with programming skills, Linux user since late 1990s, Open source supporter. Coding with Python, NodeJS (Meteor, VueJS, Express, D3, PhantomJS), SmallTalk & R language.