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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

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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