Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

Journal: International journal of computer assisted radiology and surgery
PMID: 38849632

Abstract

OBJECTIVES: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor.

Authors

  • Maria Beser-Robles
    Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain. maria_beser@iislafe.es.
  • Jaime Castellá-Malonda
    Department of Radiology, Hospital Universitario y Politécnico de La Fe, Valencia, Spain.
  • Pedro Miguel Martínez-Gironés
    Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain.
  • Adrián Galiana-Bordera
    Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain.
  • Jaime Ferrer-Lozano
    Department of Pathology, Hospital Universitario y Politécnico de La Fe, Valencia, Spain.
  • Gloria Ribas-Despuig
    Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain.
  • Regina Teruel-Coll
    Department of Radiology, Hospital Universitario y Politécnico de La Fe, Valencia, Spain.
  • Leonor Cerdá-Alberich
    Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Av. Fernando Abril Martorell 106, Torre E, 46026, Valencia, Spain.
  • Luis Marti-Bonmati
    QUIBIM SL, Valencia, Spain.

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