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.

翻译：脑肿瘤仍然是全球性的重大健康挑战，亟需诊断技术与治疗方法的进步。针对儿童患者等特定年龄群体对专用分割模型日益增长的需求，本研究探索了利用磁共振成像（MRI）模态的深度学习技术部署。通过引入基于ONet和改进版UNet的新型集成方法，结合创新性损失函数，本研究为BraTS-PEDs 2023挑战赛实现了精准分割模型。数据增强策略（包括单一变换与复合变换）确保了模型在不同扫描协议下的鲁棒性和准确性。集成策略整合了ONet与UNet模型，在捕捉MRI图像特定特征及建模其多维度特性方面展现出更优效果，最终在强化肿瘤、肿瘤核心及全肿瘤标签上分别取得了0.52、0.72和0.78的病灶级Dice评分。视觉比较进一步证实了集成方法在肿瘤区域精确覆盖方面的优越性。结果表明，这一基于各模型独特优势的先进集成方法，为提升儿童脑肿瘤诊断准确性及优化治疗方案提供了广阔前景。