SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU132034" target="_blank" >RIV/00216305:26220/19:PU132034 - isvavai.cz</a>
Result on the web
<a href="http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf" target="_blank" >http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK
Original language description
Manual segmentation of cartilage tissue in micro CT images of mouse embryos is a very time-consuming process and significantly increases the time required for the research of mammal facial structure development. It is possible to solve this problem by using a fully-automatic segmentation algorithm. In this paper, a fully-automatic segmentation method is proposed using a convolutional neural network trained on manually segmented data. The architecture of the proposed convolutional network is based on the U-Net architecture with its encoding part substituted for the encoding part of the VGG16 classification convolutional neural network pre-trained on the ImageNet database of labelled images. The proposed network achieves average Dice coefficient 0.88 in comparison to manually segmented images.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
<a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 25th Conference STUDENT EEICT 2019
ISBN
978-80-214-5735-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
191-194
Publisher name
Brno University of Technology
Place of publication
Brno
Event location
Brno
Event date
Apr 25, 2019
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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