AUTOMATED SEGMENTATION AND SHAPE ANALYSIS IN PEDIATRIC BRAIN TUMOR SURVIVORS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在圣犹大儿童研究医院放射科学部的翻译影像研究部，我们目前专注于儿童癌症幸存者的治疗所致脑损伤的特征、神经结构的组织和完整性以及神经认知表现。在这个框架内，我们正在进行的几个项目致力于图像处理算法的设计，以定量分析患者大脑发育的不同结构。 我们项目的中心思想是开发一种基于图谱的自动方法，在这种方法中，健康志愿者的手动脑结构描绘支持我们的患者磁共振扫描中模拟结构的准确分割。这些分段的结构然后被处理以研究可归因于正常发育、疾病或治疗的形状变化，以这种方式提供对测试假设的参考，该测试假设纵向地将体积和局部形状变化与患者的神经认知评估相关联。 作为一种严重依赖于对正常健康人群的大脑结构进行手动分割的统计学方法，我们的研究将极大地受益于最广泛的正常3-D磁共振扫描，这将使我们能够将特定患者与一组年龄范围很近的正常人联系起来，或者根据性别和/或侧向对其进行适当分类。我们最高的期望是，我们研究中提出的方法学提供了有价值的科学标准，可以支持治疗计划，并最终被纳入我们医院的治疗方案中。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In the Division of Translational Imaging Research, a branch of St. Jude Children's Research Hospital Radiological Sciences department, we are currently focused on the characterization of therapy-induced brain damage, neurostructural organization and integrity, and neurocognitive performance in pediatric cancer survivors. Within this framework, several of our ongoing projects pursue the design of image processing algorithms that quantitatively analyze different structures of the patients developing brain. The central idea of our project is to develop an atlas-based automatic method in which manual delineations of healthy volunteers brain structures support the accurate segmentation of analog structures in our patients MR scans. These segmented structures are then processed to study shape changes attributable either to normal development, disease, or treatment, providing in this way a reference to test hypotheses that longitudinally correlate volumetric and local shape changes to the patients neurocognitive evaluations. Being a statistical approach that heavily relies on the manual segmentation of brain structures of a normal healthy population, our study would greatly benefit from the broadest base of normal 3-D MR scans, which would allow us to relate a particular patient to a set of normal individuals within an close age range, or properly classified in terms of sex, and/or laterality. Our highest expectation is that the methodology proposed in our study, provides valuable scientific criteria that can support treatment planning and ultimately be incorporated in our hospital's treatment protocols.