Automated ensemble method for pediatric brain tumor segmentation
arxiv(2023)
摘要
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.
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