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

Autores de IIS La Fe

• María Beser Robles

  Autor

• Jaime Ferrer Lozano

  Autor

• Leonor Cerdá Alberich

  Autor

• 

  Luis Martí Bonmatí

  Autor

Participantes ajenos a IIS La Fe

• Castella-Malonda, Jaime
• Martinez-Girones, Pedro Miguel
• Galiana-Bordera, Adrian
• Ribas-Despuig, Gloria
• Teruel-Coll, Regina

Grupos

• Investigacion Biomédica en Imagen (GIBI 2-30)

  Investigador Principal

  Leonor Cerdá Alberich

Abstract

OBJECTIVES: In patients having naive 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. MATERIAL AND METHODS: A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network's performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network's performance utilized Dice scores, precision, and sensitivity metrics. RESULTS: Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce+T2w+FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively. CONCLUSIONS: The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation.

Keywords

• Glioblastoma; Automatic segmentation; Autoencoder; Dice score; Deep learning