儿科脑肿瘤分割的自动化集成方法
Automated ensemble method for pediatric brain tumor segmentation
AI总结：
针对儿科脑肿瘤分割，提出结合ONet与改进UNet的集成方法及创新损失函数，在BraTS-PEDs 2023上取得高Dice分数，提升诊断与治疗规划精度。
摘要：
脑肿瘤仍然是一个关键的全球健康挑战，需要诊断技术和治疗方法的进步。肿瘤或其复发通常需要在影像学研究中被识别，并与正常脑组织区分开来。为了满足对特定年龄分割模型日益增长的需求，特别是针对儿科患者，本研究探索了使用磁共振成像（MRI）模态的深度学习技术的部署。通过引入一种使用ONet和修改版UNet的新型集成方法，并结合创新的损失函数，本研究为BraTS-PEDs 2023挑战赛实现了精确的分割模型。数据增强，包括单一和复合变换，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和UNet模型，在捕获特定特征和建模MRI图像的不同方面显示出更高的有效性，导致在未见验证数据上，针对“增强肿瘤”、“肿瘤核心”和“全肿瘤”标签的病变级Dice分数分别为0.52、0.72和0.78，在最终测试数据上分别为0.55、0.70和0.79。视觉比较进一步证实了集成方法在准确肿瘤区域覆盖方面的优越性。结果表明，这种基于个体模型独特优势的先进集成方法，为增强儿科脑肿瘤的诊断准确性以及有效的治疗规划和监测提供了有前景的前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
