Publication result detail

Implementation of a deep learning model for vertebral segmentation in CT data

BLAŽKOVÁ, L.; NOHEL, M.

Original Title

Implementation of a deep learning model for vertebral segmentation in CT data

English Title

Implementation of a deep learning model for vertebral segmentation in CT data

Type

Paper in proceedings (conference paper)

Original Abstract

This paper deals with the problem of vertebral segmentation in CT data with the use of deep learning approaches. Automatic segmentation of vertebrae is a very complex issue and would simplify the work of radiologists and doctors. The paper is focused on one of the models published and submitted to the Large Scale Vertebrae Segmentation Challenge (VerSe) in 2020 from C. Payer et al. – Improving Coarse to Fine Vertebrae Localisation and Segmentation with SpatialConfiguration-Net and U-Net and its implementation and modification. The model is evaluated on the corresponding public and hidden dataset. Its modification shows an improvement of the results in comparison with the published results, a mean Dice score improved from 0.9165 to 0.9302 on the public dataset and from 0.8971 to 0.9264 on the hidden dataset.

English abstract

This paper deals with the problem of vertebral segmentation in CT data with the use of deep learning approaches. Automatic segmentation of vertebrae is a very complex issue and would simplify the work of radiologists and doctors. The paper is focused on one of the models published and submitted to the Large Scale Vertebrae Segmentation Challenge (VerSe) in 2020 from C. Payer et al. – Improving Coarse to Fine Vertebrae Localisation and Segmentation with SpatialConfiguration-Net and U-Net and its implementation and modification. The model is evaluated on the corresponding public and hidden dataset. Its modification shows an improvement of the results in comparison with the published results, a mean Dice score improved from 0.9165 to 0.9302 on the public dataset and from 0.8971 to 0.9264 on the hidden dataset.

Keywords

deep learning, convolutional neural networks, vertebrae segmentation, segmentation, spine, vertebra, CT, computed tomography

Key words in English

deep learning, convolutional neural networks, vertebrae segmentation, segmentation, spine, vertebra, CT, computed tomography

Authors

BLAŽKOVÁ, L.; NOHEL, M.

Released

25.04.2023

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno, Czech Republic

ISBN

978-80-214-6154-3

Book

Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers

Edition

1

ISBN

2788-1334

Periodical

Proceedings II of the Conference STUDENT EEICT

State

Czech Republic

Pages from

41

Pages to

44

Pages count

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

BibTex

@inproceedings{BUT184276,
  author="Lenka {Blažková} and Michal {Nohel}",
  title="Implementation of a deep learning model for vertebral segmentation in CT data",
  booktitle="Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers",
  year="2023",
  series="1",
  journal="Proceedings II of the Conference STUDENT EEICT",
  pages="41--44",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno, Czech Republic",
  doi="10.13164/eeict.2023.41",
  isbn="978-80-214-6154-3",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf"
}