MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU136707" target="_blank" >RIV/00216305:26220/20:PU136707 - isvavai.cz</a>
Result on the web
<a href="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_1.pdf" target="_blank" >https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING
Original language description
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 chosen 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 processing of three-dimensional data. There are also methods for data preprocessing which were applied for image multiclass segmentation. Final part of paper evaluates results which were achieved with chosen method.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů