Implementation of a deep learning model for segmentation of multiple myeloma in CT data
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Date
2024
Authors
Gálík, Pavel
Nohel, Michal
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
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.
Description
Citation
Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 105-108. ISBN 978-80-214-6231-1
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf
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Peer-reviewed
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en
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Defence
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií