Comparison of Spine Segmentation Algorithms on Clinical Data from Spectral CT of Patients with Multiple Myeloma

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00081573" target="_blank" >RIV/00159816:_____/24:00081573 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/24:PU149066
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-49062-0_34" target="_blank" >http://dx.doi.org/10.1007/978-3-031-49062-0_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-49062-0_34" target="_blank" >10.1007/978-3-031-49062-0_34</a>

Result language
angličtina
Original language name
Comparison of Spine Segmentation Algorithms on Clinical Data from Spectral CT of Patients with Multiple Myeloma
Original language description
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.
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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