Implementation of a deep learning model for segmentation of multiple myeloma in CT data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151704" target="_blank" >RIV/00216305:26220/24:PU151704 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Implementation of a deep learning model for segmentation of multiple myeloma in CT data
Original language description
This paper deals with the implementation of a deep learning model for spinal tumor segmentation of multiple myeloma patients in CT data. Deep learning is becoming an important part of developing computer-aided detection and diagnosis systems. In this study, a database of 25 patients who were imaged on spectral CT and for whom different parametric images (conventional CT, virtual monoenergetic images, calcium suppression images) were reconstructed, was used. Three convolutional neural network models based on the nnU-Net framework for lytic lesion segmentation were trained on the selected data. The results were evaluated on a test database and the trained models were compared.
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

Article name in the collection
Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers
ISBN
978-80-214-6231-1
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
105-108
Publisher name
Brno University of Technology, Faculty of Electrical Engineering and Communication
Place of publication
Brno, Czech Republic
Event location
Brno
Event date
Apr 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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