Publication 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
Type
conference paper
Language
English
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.
Keywords
deep learning, convolutional neural networks, vertebrae segmentation, segmentation, spine, vertebra, CT, computed tomography
Authors
BLAŽKOVÁ, L.; NOHEL, M.
Released
25. 4. 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
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",
issn="2788-1334",
url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf"
}