Segmentace nádorů mozku v MRI datech s využitím hloubkového učení
| but.committee | prof. Ing. Ivo Provazník, Ph.D. (předseda) Ing. Marina Ronzhina, Ph.D. (místopředseda) Ing. Vratislav Harabiš, Ph.D. (člen) Ing. Jan Odstrčilík, Ph.D. (člen) Ing. Jiří Sekora (člen) | cs |
| but.defence | Student prezentoval výsledky své práce a komise byla seznámena s posudky. Student presented the results of his master thesis and the committee members were acquainted with the reviews. Ing. Ronzhina položila otázku, zda student měnil nastavené parametry. Ing. Ronzhina asked if the student tried changing parameters. Prof. Provazník položil otázku, zda je počet pixelů v jednotlivých osách odlišný. Prof. Provazník asked if number of pixels in each of the axes was different. Student defended the master thesis with reservations and answered the questions. | cs |
| but.jazyk | angličtina (English) | |
| but.program | Electrical, Electronic, Communication and Control Technology | cs |
| but.result | práce byla úspěšně obhájena | cs |
| dc.contributor.advisor | Chmelík, Jiří | en |
| dc.contributor.author | Ustsinau, Usevalad | en |
| dc.contributor.referee | Odstrčilík, Jan | en |
| dc.date.accessioned | 2020-06-18T06:59:18Z | |
| dc.date.available | 2020-06-18T06:59:18Z | |
| dc.date.created | 2020 | cs |
| dc.description.abstract | The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the structure of convolutional neural networks and how they implemented into biomedical image analysis, besides it was taken a popular modification of U-Net and tested on two loss-functions. As far as segmentation quality plays a highly important role for doctors, in experiment part it was paid significant attention to training quality and prediction results of the model. The experiment has shown the effectiveness of the provided algorithm and performed 100 training cases with the following analysis through the similarity. The proposed outcome gives us certain ideas for future improving the quality of image segmentation via deep learning techniques. | en |
| dc.description.abstract | The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the structure of convolutional neural networks and how they implemented into biomedical image analysis, besides it was taken a popular modification of U-Net and tested on two loss-functions. As far as segmentation quality plays a highly important role for doctors, in experiment part it was paid significant attention to training quality and prediction results of the model. The experiment has shown the effectiveness of the provided algorithm and performed 100 training cases with the following analysis through the similarity. The proposed outcome gives us certain ideas for future improving the quality of image segmentation via deep learning techniques. | cs |
| dc.description.mark | D | cs |
| dc.identifier.citation | USTSINAU, U. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2020. | cs |
| dc.identifier.other | 126757 | cs |
| dc.identifier.uri | http://hdl.handle.net/11012/189317 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights | Standardní licenční smlouva - přístup k plnému textu bez omezení | cs |
| dc.subject | Medical Imaging | en |
| dc.subject | Brain Tumour | en |
| dc.subject | Convolutional Neural Network | en |
| dc.subject | Segmentation | en |
| dc.subject | U-Net | en |
| dc.subject | Medical Imaging | cs |
| dc.subject | Brain Tumour | cs |
| dc.subject | Convolutional Neural Network | cs |
| dc.subject | Segmentation | cs |
| dc.subject | U-Net | cs |
| dc.title | Segmentace nádorů mozku v MRI datech s využitím hloubkového učení | en |
| dc.title.alternative | Segmentation of brain tumours in MRI images using deep learning | cs |
| dc.type | Text | cs |
| dc.type.driver | masterThesis | en |
| dc.type.evskp | diplomová práce | cs |
| dcterms.dateAccepted | 2020-06-17 | cs |
| dcterms.modified | 2020-06-19-08:23:34 | cs |
| eprints.affiliatedInstitution.faculty | Fakulta elektrotechniky a komunikačních technologií | cs |
| sync.item.dbid | 126757 | en |
| sync.item.dbtype | ZP | en |
| sync.item.insts | 2021.11.12 13:12:01 | en |
| sync.item.modts | 2021.11.12 12:14:11 | en |
| thesis.discipline | Biomedical and Ecological Engineering | cs |
| thesis.grantor | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrství | cs |
| thesis.level | Inženýrský | cs |
| thesis.name | Ing. | cs |
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