Publication detail
SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK
MATULA, J.
Original Title
SEGMENTATION OF CARTILAGE TISSUE IN MICRO CT IMAGES OF MOUSE EMBRYOS WITH MODIFIED U-NET CONVOLUTIONAL NEURAL NETWORK
Type
conference paper
Language
English
Original Abstract
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.
Keywords
segmentation; cartilage; convolutional neural networks; deep learning
Authors
MATULA, J.
Released
25. 4. 2019
Publisher
Brno University of Technology
Location
Brno
ISBN
978-80-214-5735-5
Book
Proceedings of the 25th Conference STUDENT EEICT 2019
Edition number
první
Pages from
191
Pages to
194
Pages count
4
URL
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"
}