Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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",
  annote="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.",
  address="IEEE",
  booktitle="2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
  chapter="157840",
  doi="10.1109/EMBC.2019.8856528",
  howpublished="print",
  institution="IEEE",
  year="2019",
  month="october",
  pages="2407--2410",
  publisher="IEEE",
  type="conference paper"
}