AUTOMATED SEGMENTATION AND SHAPE ANALYSIS IN PEDIATRIC BRAIN TUMOR SURVIVORS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 在圣裘德儿童研究医院放射科学部的一个分支--转化成像研究部，我们目前专注于治疗引起的脑损伤、神经结构组织和完整性以及儿童癌症幸存者的神经认知表现的表征。 在这个框架内，我们正在进行的几个项目追求图像处理算法的设计，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，其中健康志愿者大脑结构的手动描绘支持我们患者MR扫描中模拟结构的准确分割。这些分割的结构，然后进行处理，以研究形状变化归因于正常的发展，疾病，或治疗，以这种方式提供一个参考，以测试假设纵向相关的体积和局部形状变化的患者神经认知评估。 作为一种严重依赖于正常健康人群大脑结构手动分割的统计方法，我们的研究将极大地受益于最广泛的正常3-D MR扫描基础，这将使我们能够将特定患者与一组年龄范围内的正常个体联系起来，或者根据性别和/或偏侧性进行适当分类。 我们的最高期望是，我们研究中提出的方法提供了有价值的科学标准，可以支持治疗计划，并最终纳入我们医院的治疗方案。

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.