Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine

Nagyová, Miriam; Nohel, Michal

Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine

Files

8-eeict-2024-II.pdf(2.93 MB)

Date

2024

Authors

Nagyová, Miriam

Nohel, Michal

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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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

Citation

Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 8-11. ISBN 978-80-214-6230-4
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence