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Brain tumors remain a critical global health challenge, necessitatingadvancements in diagnostic techniques and treatment methodologies. In responseto the growing need for age-specific segmentation models, particularly forpediatric patients, this study explores the deployment of deep learningtechniques using magnetic resonance imaging (MRI) modalities. By introducing anovel ensemble approach using ONet and modified versions of UNet, coupled withinnovative loss functions, this study achieves a precise segmentation model forthe BraTS-PEDs 2023 Challenge. Data augmentation, including both single andcomposite transformations, ensures model robustness and accuracy acrossdifferent scanning protocols. The ensemble strategy, integrating the ONet andUNet models, shows greater effectiveness in capturing specific features andmodeling diverse aspects of the MRI images which result in lesion_wise dicescores of 0.52, 0.72 and 0.78 for enhancing tumor, tumor core and whole tumorlabels respectively. Visual comparisons further confirm the superiority of theensemble method in accurate tumor region coverage. The results indicate thatthis advanced ensemble approach, building upon the unique strengths ofindividual models, offers promising prospects for enhanced diagnostic accuracyand effective treatment planning for brain tumors in pediatric brains.

Automated Ensemble-Based Segmentation of Pediatric Brain Tumors: A Novel Approach Using the CBTN-CONNECT-ASNR-MICCAI BraTS-PEDs 2023 Challenge Data