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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148717" target="_blank" >RIV/00216305:26220/23:PU148717 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2023.41" target="_blank" >10.13164/eeict.2023.41</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-80-214-6154-3

ISSN

2788-1334

e-ISSN

—

Počet stran výsledku

4

Strana od-do

41-44

Název nakladatele

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo vydání

Brno, Czech Republic

Místo konání akce

Brno

Datum konání akce

25. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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