Detail publikace

SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK

MATULA, J.

Originální název

SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

segmentation; cartilage; convolutional neural networks; deep learning

Autoři

MATULA, J.

Vydáno

25. 4. 2019

Nakladatel

Brno University of Technology

Místo

Brno

ISBN

978-80-214-5735-5

Kniha

Proceedings of the 25th Conference STUDENT EEICT 2019

Číslo edice

první

Strany od

191

Strany do

194

Strany počet

4

URL

http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf

BibTex

@inproceedings{BUT156825,
  author="Jan {Matula}",
  title="SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK",
  booktitle="Proceedings of the 25th  Conference STUDENT EEICT 2019",
  year="2019",
  number="první",
  pages="191--194",
  publisher="Brno University of Technology",
  address="Brno",
  isbn="978-80-214-5735-5",
  url="http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf"
}