Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151702" target="_blank" >RIV/00216305:26220/24:PU151702 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/eeict.2024.8" target="_blank" >10.13164/eeict.2024.8</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
Original language description
This paper focuses on training a deep learning model for vertebral body segmentation of the lumbar spine. The nnU-Net model was trained and tested on a publicly available dataset LumVBCanSeg consisting of 185 lumbar CT scans. Dice coefficient was used to evaluate the accuracy of the trained model. The mean Dice coefficient of the testing dataset was 0.949 with a standard deviation of 0.103. The model was also tested on clinical data containing various abnormalities, such as lytic lesions in multiple myeloma patients and metallic implants. Results were evaluated visually. While the model showed high accuracy on the testing dataset, the results on scans with anomalies showed a decline in accuracy.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers
ISBN
978-80-214-6230-4
ISSN
2788-1334
e-ISSN
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Number of pages
4
Pages from-to
8-11
Publisher name
Brno University of Technology, Faculty of Electrical Engineering and Communication
Place of publication
Brno, Czech Republic
Event location
Brno
Event date
Apr 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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