基于磁共振图像的帕金森病计算机辅助诊断研究进展

Progress in Computer-Aided Diagnosis of Parkinson’s Disease Based on Magnetic Resonance Imaging

近年来,由于帕金森病(PD)的临床复杂性与多模态磁共振(MR)图像的高维性,如何有效挖掘图像中特异性标记PD的影像生物标志物、建立高效的PD计算机辅助诊断(CAD)模型是研究中极具挑战性的问题。综述目前国内外研究进展,进一步分析MR多模态特征提取、特征选择、分类器模型等传统机器学习方法建立CAD模型的关键技术,并简要概述基于深度学习方法在早期PD分类诊断中的应用。指出基于多模态MR图像,采用机器学习或深度学习方法构建CAD模型,能够客观、准确地识别PD患者,对提高早期PD诊断的准确性具有很大价值和应用前景。今后研究应更深入挖掘多模态MR图像中的潜在标记PD的影像生物指标,开发更高阶的CAD模型,以辅助早期PD的临床智能诊断。

In recent years,due to the clinical complexity of Parkinson ’s disease( PD) and the highdimensional nature of multi-mode magnetic resonance( MR) images,how to effectively use the specific image biomarkers and establish an efficient Computer-aided Diagnosis( CAD) model for disease diagnosis is a challenging problem in PD research. This paper reviewed the research progress,and summarized key techniques of CAD modeling based on traditional machine learning methods such as feature extraction,feature selection and the classifier model. This paper also briefly introduced the recent research and application of deep learning in early PD classification diagnosis. It is pointed out that based on multi-modal images,CAD model constructed by machine learning or deep learning can recognize PD patients and normal people objectively and accurately,which has great value and application prospect to improve the accuracy of early PD diagnosis. Future researches should be carried out to explore the potential biomarkers of PD in multi-modality images,and to develop higherorder CAD models to assist the clinical intelligent diagnosis of early PD.