Original title: Implementation of a deep learning model for vertebral segmentation in CT data
Authors: Blažkova, Lenka ; Nohel, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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: computedtomography; convolutional neural networks,vertebrae segmentation; CT; deep learning; segmentation; spine; vertebra
Host item entry: Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers, ISBN 978-80-214-6154-3, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/210708

Permalink: http://www.nusl.cz/ntk/nusl-531814


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers