Detail publikace

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

SOLÁR, P. VALEKOVÁ, H. MARCOŇ, P. MIKULKA, J. BARÁK, M. HENDRYCH, M. STRÁNSKÝ, M. SIRŮČKOVÁ, K. KOSTIAL, M. HOLÍKOVÁ, K. BRYCHTA, J. JANČÁLEK, R.

Originální název

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs’ compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.

Klíčová slova

DWI, ADC, brain lesions, segmentation, classification, artifical intelligence

Autoři

SOLÁR, P.; VALEKOVÁ, H.; MARCOŇ, P.; MIKULKA, J.; BARÁK, M.; HENDRYCH, M.; STRÁNSKÝ, M.; SIRŮČKOVÁ, K.; KOSTIAL, M.; HOLÍKOVÁ, K.; BRYCHTA, J.; JANČÁLEK, R.

Vydáno

15. 7. 2023

Nakladatel

Springer Nature

ISSN

2045-2322

Periodikum

Scientific Reports

Ročník

13

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany počet

11

URL

https://doi.org/10.1038/s41598-023-38542-7

Plný text v Digitální knihovně

http://hdl.handle.net/11012/244998

BibTex

@article{BUT184472,
  author="Peter {Solár} and Hana {Valeková} and Petr {Marcoň} and Jan {Mikulka} and Martin {Barák} and Michal {Hendrych} and Matyáš {Stránský} and Kateřina {Novotná} and Martin {Kostial} and Klára {Holíková} and Jindřich {Brychta} and Radim {Jančálek}",
  title="Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics",
  journal="Scientific Reports",
  year="2023",
  volume="13",
  number="1",
  pages="11",
  doi="10.1038/s41598-023-38542-7",
  issn="2045-2322",
  url="https://doi.org/10.1038/s41598-023-38542-7"
}