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

Shashidhar Reddy Javaji, Sovesh Mohapatra, advait gosai

Abstract:

Brain tumors remain a critical global health challenge, necessitating advancements in diagnostic techniques and treatment methodologies. A tumor or its recurrence often needs to be identified in imaging studies and differentiated from normal brain tissue. In response to
the growing need for age-specific segmentation models, particularly for
pediatric patients, this study explores the deployment of deep learning
techniques using magnetic resonance imaging (MRI) modalities. By introducing a novel ensemble approach using ONet and modified versions
of UNet, coupled with innovative loss functions, this study achieves a
precise segmentation model for the BraTS-PEDs 2023 Challenge. Data
augmentation, including both single and composite transformations, ensures model robustness and accuracy across different scanning protocols.
The ensemble strategy, integrating the ONet and UNet models, shows
greater effectiveness in capturing specific features and modeling diverse
aspects of the MRI images which result in lesion wise Dice scores of 0.52,
0.72 and 0.78 on unseen validation data and scores of 0.55, 0.70, 0.79 on
final testing data for the ”enhancing tumor”, ”tumor core” and ”whole
tumor” labels respectively. Visual comparisons further confirm the superiority of the ensemble method in accurate tumor region coverage. The
results indicate that this advanced ensemble approach, building upon
the unique strengths of individual models, offers promising prospects
for enhanced diagnostic accuracy and effective treatment planning and
monitoring for brain tumors in pediatric brains.