论文标题
一种用于研究基于DTI纤维区域的神经退行性疾病的预测视觉分析系统
A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts
论文作者
论文摘要
扩散张量成像（DTI）已用于研究神经退行性疾病对神经途径的影响，这可能会导致对这些疾病的更可靠和早期诊断，并更好地了解它们如何影响大脑。我们介绍了一个智能视觉分析系统，用于根据其标记的DTI纤维道数据和相应的统计数据来研究患者群体。该系统的AI增强界面引导用户通过有组织的整体分析空间，包括统计特征空间，物理空间以及患者在不同组上的空间。我们使用自定义的机器学习管道来帮助缩小这一较大的分析空间，然后通过一系列链接的可视化务实地探索它。我们使用来自帕金森氏症进步标记倡议的研究数据库的实际数据进行了几项案例研究。
Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce an intelligent visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's AI-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space, and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using real data from the research database of Parkinson's Progression Markers Initiative.
