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Whole Slide Image Analysis

Sophisticated Image Analysis Algorithms

Orbit has many build-in image analysis algorithms. Tissue quantification using machine learning techniques, object / cell segmentation, and object classification are the basic ones. Region of interest (ROI) can be defined by manual annotations or via a trainable exclusion map. Everything can be combined.


All algorithms are build to work on really big images, up to gigapixel images, especially whole slide scans.
This is possible due to a tile-based processing pipeline and combination with the use of different resolutions of the image.

Connectivity: OMERO & Spark connectors

You can use your existing image server, e.g. it has been designed to work great with Omero.
Orbit can also run in stand-alone mode and open whole slide images like SVS, NDPI, SCN, ….

A Spark infrastructure can be used as scaleout infrastructure to distribute computation intensive tasks.

Scripting & Extentions & Connectors

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Individual image server and scaleout connectors can also be created by implementing clear interfaces.

Version 3.05: Omero configuration dialog, HiDPI bugfix, Deep Learning improvements.
Give it a try!

What it is

A very versatile image analysis software
It's completely free, really!
And it will stay for free.
No demo, it's a full version.

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Orbit Image Analysis is a free open source software with the focus to quantify big images like whole slide scans.

It can connect to image servers, e.g. Omero.
Analysis can be done on your local computer or via scaleout functionality in a distrubuted computing environment like a Spark cluster.

Spring Single MDRW Sharp Shoes Elegant Shallow 35 Lady Thin Bridesmaid 10Cm Shoes Wedding Light Heel Gray Heels Work Leisure Sophisticated image analysis algorithms incl. tissue quantification using machine learning, object segmentation and classification are build in. In addition a versatile API allows you to enhance Orbit and to run your own scripts.

Image Analysis

Sophisticated image analysis features

Object segmentation

Segment objects like cells or nerves.
Object detection based on trainable foreground / background classes. Overlapping object, e.g. cell clusters can be devided afterwards. Features of objects (shape factors, area, intensities, …) can be computed and reported or used for object classification.

Object Classification

Assign classes to objects based on their features.
Segmented objects can be classified based on their features like size, shape factors or intensities. For instance, it can be distinguished between stained and not stained cells or between round and edgy objects. This classification is based on machine learning; the user can specify classes by selecting examples.
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Annotations & ROI

Annotations and trainable exclusion maps for ROI definition.
The ROI (region of interest) can be defined by manual annotations or a trainable exclusion map which defines “the good” area of your tissue. Both methods can be combined.

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