Comparison of full-size and patches-based learning approaches for aneurysm segmentation in TOF-MRI data

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorVývoda, J.
dc.contributor.authorJakubíček, R.
dc.date.accessioned2023-04-25T10:17:08Z
dc.date.available2023-04-25T10:17:08Z
dc.date.issued2022cs
dc.description.abstractThe paper is interested in segmentation of intracranial aneurysms. Intracranial aneurysms are life-threatening issue. In this paper there are proposed two methods for this segmentation problem. First one is segmentation with use of full size images, the other one uses patches of the image, which could help decrease the ration between pixels representing background and pixels representing aneurysms. Data from ADAM challenge 2020 are used to train and evaluate these approaches. Using full images show better results in dice coefficient, which is 0.16 greater, then patched image approach.en
dc.formattextcs
dc.format.extent247-250cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 28st Conference STUDENT EEICT 2022: General papers. s. 247-250. ISBN 978-80-214-6029-4cs
dc.identifier.isbn978-80-214-6029-4
dc.identifier.urihttp://hdl.handle.net/11012/209338
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 28st Conference STUDENT EEICT 2022: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectIntracranial aneurysmen
dc.subjectaneurysmen
dc.subjectmachine learningen
dc.subjectdetectionen
dc.subjectmagnetic resonanceen
dc.subjectU-neten
dc.subjectsegmentationen
dc.titleComparison of full-size and patches-based learning approaches for aneurysm segmentation in TOF-MRI dataen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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