Multiclass Segmentation Of 3d Medical Data With Deep Learning
| but.event.date | 23.04.2020 | cs |
| but.event.title | Student EEICT 2020 | cs |
| dc.contributor.author | Slunsky, Tomas | |
| dc.date.accessioned | 2021-07-15T11:17:21Z | |
| dc.date.available | 2021-07-15T11:17:21Z | |
| dc.date.issued | 2020 | cs |
| dc.description.abstract | This paper deals with multiclass image segmentation using convolutional neural networks. The theoretical part of paper focuses on image segmentation. There are basics principles of neural networks and image segmentation with more types of approaches. In practical part the Unet architecture is choosen and is described for image segmentation more. U-net was applied for medicine dataset which consist from 3D MRI of human brain. There is processing procedure which is more described for image proccesing of three-dimmensional data. There are also methods for data preproccessing which were applied for image multiclass segmentation. Final part of paper evaluates results which were achieved with choosen method. | en |
| dc.format | text | cs |
| dc.format.extent | 325-329 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers. s. 325-329. ISBN 978-80-214-5867-3 | cs |
| dc.identifier.isbn | 978-80-214-5867-3 | |
| dc.identifier.uri | http://hdl.handle.net/11012/200588 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers | en |
| dc.relation.uri | https://conf.feec.vutbr.cz/eeict/EEICT2020 | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | deep learning | en |
| dc.subject | convolutional neural network | en |
| dc.subject | multi-class image segmentation | en |
| dc.title | Multiclass Segmentation Of 3d Medical Data With Deep Learning | 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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