Publication detail

Deep-learning-based fully automatic spine centerline detection in CT data

JAKUBÍČEK, R. CHMELÍK, J. OUŘEDNÍČEK, P. JAN, J.

Original Title

Deep-learning-based fully automatic spine centerline detection in CT data

Type

conference paper

Language

English

Original Abstract

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.

Keywords

CT; spine centerline; machine learning

Authors

JAKUBÍČEK, R.; CHMELÍK, J.; OUŘEDNÍČEK, P.; JAN, J.

Released

7. 10. 2019

Publisher

IEEE

Location

Berlin, Germany

ISBN

978-1-5386-1312-2

Book

2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Edition number

19

ISBN

1557-170X

Periodical

Proceedings IEEE EMBC

Year of study

19

State

United States of America

Pages from

2407

Pages to

2410

Pages count

4

URL

https://ieeexplore.ieee.org/document/8856528

BibTex

@inproceedings{BUT157840,
  author="Roman {Jakubíček} and Jiří {Chmelík} and Petr {Ouředníček} and Jiří {Jan}",
  title="Deep-learning-based fully automatic spine centerline detection in CT data",
  booktitle="2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
  year="2019",
  journal="Proceedings IEEE EMBC",
  volume="19",
  number="19",
  pages="2407--2410",
  publisher="IEEE",
  address="Berlin, Germany",
  doi="10.1109/EMBC.2019.8856528",
  isbn="978-1-5386-1312-2",
  issn="1557-170X",
  url="https://ieeexplore.ieee.org/document/8856528"
}