Publication detail
Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
ČURILLOVÁ, M. NOHEL, M.
Original Title
Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
Type
conference paper
Language
English
Original Abstract
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.
Keywords
multiple myeloma, osteolytic lesions, nnU-Net, segmentation
Authors
ČURILLOVÁ, M.; NOHEL, M.
Released
23. 4. 2024
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno, Czech Republic
ISBN
978-80-214-6230-4
Book
Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers
Edition
1
ISBN
2788-1334
Periodical
Proceedings II of the Conference STUDENT EEICT
State
Czech Republic
Pages from
8
Pages to
11
Pages count
4
URL
BibTex
@inproceedings{BUT189059,
author="Miriam {Čurillová} and Michal {Nohel}",
title="Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine",
booktitle="Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers",
year="2024",
series="1",
journal="Proceedings II of the Conference STUDENT EEICT",
pages="8--11",
publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
address="Brno, Czech Republic",
doi="10.13164/eeict.2024.8",
isbn="978-80-214-6230-4",
issn="2788-1334",
url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf"
}