Publication result detail

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

GÁLÍK, P.; NOHEL, M.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original 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.

English 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.

Keywords

multiple myeloma, computed tomography, deep learning, nnU-Net, segmentation, monoenergetic image, calcium suppress image

Key words in English

multiple myeloma, computed tomography, deep learning, nnU-Net, segmentation, monoenergetic image, calcium suppress image

Authors

GÁLÍK, P.; NOHEL, M.

RIV year

2025

Released

23.04.2024

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno, Czech Republic

ISBN

978-80-214-6231-1

Book

Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers

Edition

1

Pages from

105

Pages to

108

Pages count

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf

BibTex

@inproceedings{BUT189071,
  author="Pavel {Gálík} and Michal {Nohel}",
  title="Implementation of a deep learning model for segmentation of multiple myeloma in CT data",
  booktitle="Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers",
  year="2024",
  series="1",
  pages="105--108",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno, Czech Republic",
  isbn="978-80-214-6231-1",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf"
}