Supervised Segmentation For 3D Slicer

Chalupa, Daniel

Supervised Segmentation For 3D Slicer

but.event.date	27.04.2017	cs
but.event.title	Student EEICT 2017	cs
dc.contributor.author	Chalupa, Daniel
dc.date.accessioned	2020-05-07T09:40:29Z
dc.date.available	2020-05-07T09:40:29Z
dc.date.issued	2017	cs
dc.description.abstract	The purpose of this work is to introduce an extendable framework for training and usage of machine learning algorithms. This framework is bundled in an extension for 3D Slicer that is to be used for medical images segmentation. An example usage of the extension is also provided.	en
dc.format	text	cs
dc.format.extent	296-298	cs
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Proceedings of the 23st Conference STUDENT EEICT 2017. s. 296-298. ISBN 978-80-214-5496-5	cs
dc.identifier.isbn	978-80-214-5496-5
dc.identifier.uri	http://hdl.handle.net/11012/187112
dc.language.iso	en	cs
dc.publisher	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.relation.ispartof	Proceedings of the 23st Conference STUDENT EEICT 2017	en
dc.relation.uri	http://www.feec.vutbr.cz/EEICT/	cs
dc.rights	© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.rights.access	openAccess	en
dc.subject	3D Slicer	en
dc.subject	C++	en
dc.subject	extension	en
dc.subject	machine learning	en
dc.subject	optimization	en
dc.subject	segmentation	en
dc.subject	tomography	en
dc.title	Supervised Segmentation For 3D Slicer	en
dc.type.driver	conferenceObject	en
dc.type.status	Peer-reviewed	en
dc.type.version	publishedVersion	en
eprints.affiliatedInstitution.department	Fakulta elektrotechniky a komunikačních technologií	cs

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