Brain tumors remain a critical global health challenge, necessitating advancements in diagnostic techniques and treatment methodologies. 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 for 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 for brain tumors in pediatric brains.

该研究基于磁共振成像技术（MRI）模态，采用深度学习技术和创新的集成方法，成功实现了对儿童脑肿瘤的精确分割模型，具有提高诊断准确性和有效治疗策划的潜力。

儿童脑肿瘤的自动集成分割：使用CBTN-CONNECT-ASNR-MICCAI BraTS-PEDs 2023挑战数据的新方法