Publication detail

Comparison of spine segmentation algorithms on clinical data from spectral CT of patients with multiple myeloma

NOHEL, M. JAKUBÍČEK, R. BLAŽKOVÁ, L. VÁLEK, V. DOSTÁL, M. OUŘEDNÍČEK, P. CHMELÍK, J.

Original Title

Comparison of spine segmentation algorithms on clinical data from spectral CT of patients with multiple myeloma

Type

conference paper

Language

English

Original Abstract

This article presents an evaluation of spine segmentation models using clinical data obtained from multiple myeloma patients. The performance of the models is compared based on the classical Dice score. The results show that the Payer and nnU-Net models show the highest level of similarity in segmentation. However, when it comes to the challenging task of segmenting cervical vertebrae, the Payer algorithm provides more accurate results. On the other hand, the nnU-Net model achieves better results in cases of extensive vertebral deformation. We also observed that convolutional neural networks have problems in segmenting metal surgical implants. Research highlights the strengths and weaknesses of different models and can help select appropriate segmentation algorithms for specific clinical scenarios.

Keywords

spine segmentation, spectral CT, multiple myeloma, nnU-Net, deep learning

Authors

NOHEL, M.; JAKUBÍČEK, R.; BLAŽKOVÁ, L.; VÁLEK, V.; DOSTÁL, M.; OUŘEDNÍČEK, P.; CHMELÍK, J.

Released

4. 1. 2024

Publisher

Springer Nature Switzerland

Location

Cham

ISBN

978-3-031-49061-3

Book

MEDICON'23 & CMBEBIH'23

Edition

93

Edition number

1

ISBN

1680-0737

Periodical

IFMBE PROCEEDINGS

Year of study

93

State

Kingdom of Sweden

Pages from

309

Pages to

317

Pages count

9

URL

https://link.springer.com/chapter/10.1007/978-3-031-49062-0_34

BibTex

@inproceedings{BUT184739,
  author="Michal {Nohel} and Roman {Jakubíček} and Lenka {Blažková} and Vlastimil {Válek} and Marek {Dostál} and Petr {Ouředníček} and Jiří {Chmelík}",
  title="Comparison of spine segmentation algorithms on clinical data from spectral CT of patients with multiple myeloma",
  booktitle="MEDICON'23 & CMBEBIH'23",
  year="2024",
  series="93",
  journal="IFMBE PROCEEDINGS",
  volume="93",
  number="1",
  pages="309--317",
  publisher="Springer Nature Switzerland",
  address="Cham",
  doi="10.1007/978-3-031-49062-0\{_}34",
  isbn="978-3-031-49061-3",
  issn="1680-0737",
  url="https://link.springer.com/chapter/10.1007/978-3-031-49062-0_34"
}