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{{more citations needed|date=January 2015}}
'''Cognition Network Technology''' ('''CNT'''), also known as '''Definiens Cognition Network Technology''',<ref>{{Cite web|url=http://www.ecognition.com/sites/default/files/technology.pdf|title=Image analysis with Definiens Cognition Network Technology}}</ref> is an [[object-based image analysis]] method developed by Nobel laureate [[Gerd Binnig]] together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods.
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==History==
In 1994 Professor Gerd Binnig founded ''Definiens''. CNT was first available with the launch of the ''eCognition'' software in May 2000. In June 2010, [[Trimble Navigation Ltd]] (NASDAQ: TRMB) acquired Definiens business asset in [[earth sciences]] markets, including eCognition software,<ref>{{Cite web | publisher=Trimble / Definiens Joint Press Release
==Software==
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===Definiens Tissue Studio===
''Definiens Tissue Studio'' is a [[digital pathology]] image analysis software application based on CNT.<ref>{{cite web |url=http://www.definiens.com/technology_41_1_4.html |title=Definiens Cognition Network Technology |accessdate=2010-02-10 |url-status=dead |archiveurl=https://web.archive.org/web/20100325202459/http://www.definiens.com/technology_41_1_4.html |archivedate=2010-03-25 }}</ref>
The intended use of Definiens Tissue Studio is for biomarker [[translational research]] in formalin-fixed, paraffin-embedded tissue samples which have been treated with [[Immunohistochemistry|immunohistochemical]] staining assays, or [[H&E stain|hematoxylin and eosin]] (H&E).<ref name="auto">{{cite web |title=Definiens Tissue Studio™ |url=http://www.definiens.com/tissuestudio
The central concept behind Definiens Tissue Studio is a user interface that facilitates [[machine learning]] from example digital histopathology images in order to derive an image analysis solution suitable for the measurement of [[biomarkers]] and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments.<ref name="auto"/> The derived image analysis solution is then automatically applied to subsequent digital images in order to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive [[cancer]] and other diseases.<ref>{{Cite web |url=http://laboratorian.advanceweb.com/Article/The-Image-Analysis-Challenge.aspx |title=The Image Analysis Challenge on ADVANCE for Medical Laboratory Professionals |access-date=2016-06-28 |archive-url=https://web.archive.org/web/20110707090609/http://laboratorian.advanceweb.com/Article/The-Image-Analysis-Challenge.aspx |archive-date=2011-07-07 |url-status=dead }}</ref> Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid.<ref name="auto"/>
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