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

Blažkova, Lenka; Nohel, Michal

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

Files

41_EEICT_selected.pdf(3.51 MB)

Date

2023

Authors

Blažkova, Lenka

Nohel, Michal

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

This paper deals with the problem of vertebral segmentationin CT data with the use of deep learning approaches.Automatic segmentation of vertebrae is a very complex issueand would simplify the work of radiologists and doctors. Thepaper is focused on one of the models published and submittedto the Large Scale Vertebrae Segmentation Challenge (VerSe) in2020 from C. Payer et al. – Improving Coarse to Fine VertebraeLocalisation and Segmentation with SpatialConfiguration-Netand U-Net and its implementation and modification. The modelis evaluated on the corresponding public and hidden dataset. Itsmodification shows an improvement of the results in comparisonwith the published results, a mean Dice score improved from0.9165 to 0.9302 on the public dataset and from 0.8971 to 0.9264on the hidden dataset.

Keywords

deep learning, convolutional neural networks,vertebrae segmentation, segmentation, spine, vertebra, CT, computedtomography

Citation

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers. s. 41-44. ISBN 978-80-214-6154-3
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence