帕金森病多模态图像全自动诊断框架
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
摘要：
背景：帕金森病（PD）是一种常见的长期神经退行性疾病。虽然PD的诊断标准相对较好地定义，但目前的医学成像诊断程序对专业知识要求很高，因此需要更高集成度的基于AI的诊断算法。研究方法：在本文中，我们提出了一个自动的，端到端的，多模态诊断框架，包括分割，配准，特征生成和机器学习，处理纹状体的信息诊断PD。在拟议的框架中使用了多种模式，包括T1加权MRI和11 C-CFT PET。然后在华山医院PET中心的数据集上验证该框架的可靠性，因为该数据集包含18名正常（NL）受试者和49名PD受试者的配对T1-MRI和CFT-PET图像。结果如下：我们获得了100%的准确率为PD/NL分类任务，此外，我们进行了几个比较实验，以验证我们的框架的诊断能力。结论：实验结果表明：（1）自动分割与人工分割具有相同的分类效果;（2）多模态图像比单模态图像具有更好的预测效果;（3）体积特征与PD诊断无关。
英文摘要：
Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis.
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