Cristin-resultat-ID: 1953825
Sist endret: 12. januar 2022, 15:27
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

Bidragsytere:
  • David Nicolas Jean-Marie Bouget
  • Roelant Eijgelaar
  • André Pedersen
  • Ivar Kommers
  • Hilko Ardon
  • Frederik Barkhof
  • mfl.

Tidsskrift

Cancers
ISSN 2072-6694
e-ISSN 2072-6694
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Trykket: 2021
Volum: 13
Hefte: 18
Artikkelnummer: 4674
Open Access

Importkilder

Scopus-ID: 2-s2.0-85115054009

Beskrivelse Beskrivelse

Tittel

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

Sammendrag

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

Bidragsytere

David Nicolas Jean-Mar Bouget

Bidragsyterens navn vises på dette resultatet som David Nicolas Jean-Marie Bouget
  • Tilknyttet:
    Forfatter
    ved Helse ved SINTEF AS

Roelant Eijgelaar

  • Tilknyttet:
    Forfatter
    ved Vrije Universiteit Amsterdam Medical Center

André Pedersen

  • Tilknyttet:
    Forfatter
    ved Helse ved SINTEF AS

Ivar Kommers

  • Tilknyttet:
    Forfatter
    ved Vrije Universiteit Amsterdam Medical Center

Hilko Ardon

  • Tilknyttet:
    Forfatter
    ved Nederland
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