Detail publikace

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

ČURILLOVÁ, M. NOHEL, M.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

multiple myeloma, osteolytic lesions, nnU-Net, segmentation

Autoři

ČURILLOVÁ, M.; NOHEL, M.

Vydáno

23. 4. 2024

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno, Czech Republic

ISBN

978-80-214-6230-4

Kniha

Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers

Edice

1

ISSN

2788-1334

Periodikum

Proceedings II of the Conference STUDENT EEICT

Stát

Česká republika

Strany od

8

Strany do

11

Strany počet

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf

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"
}

Odpovědnost: Ing. Marek Strakoš