结合图像分析的非参数随机效应模型及其在临床医学数据中的应用研究

项目名称： 结合图像分析的非参数随机效应模型及其在临床医学数据中的应用研究

项目编号： No.11471024

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 数理科学和化学

项目作者： 周铁

作者单位： 北京大学

项目金额： 71万元

中文摘要： 协和医院妇科内分泌医疗数据包含大量影像资料以及高维复杂数据。同时，由于患者个体差异很大，治疗效果呈现不同的分布规律。本项目拟采用图像处理技术和非参数随机效应模型相结合的方法对协和医院妇科内分泌医疗数据建立数学模型。通过新模型设计高效, 稳定的统计和计算方法，研究国内妇科内分泌疾病患者治疗前后全身内分泌代谢功能等各项指标的变化规律，为临床治疗提供确切的基础研究依据，为进一步提高我国更年期综合征的治疗水平打下理论基础。在研究中，首先采用变分方法进行图像分析提取图像特征。然后利用经验似然和EM算法, 将结合图像信息的非参数随机效应模型产生的复杂似然函数在一定约束条件下转化为剖面似然。最后结合贝叶斯统计学和Monte Carlo模拟方法, 研究非参数、半参数模型中关心参数的统计推断，从而避免了高维积分和渐近方差估计等计算难题。

中文关键词： 医学图像分析；随机效应模型；混合模型；非参数贝叶斯

英文摘要： Large amount of valuable clinical trial and treatment datasets about endocrine diseases has been accumulated in Peking Union Medical College Hospital, which includes many images and high-dimensional data. From the data we found that due to the diversity of the individual patients, the distribution law of the treatment effects are quite complex . This project intends to build some mathematical models from the datasets by combining the image processing techniques and the nonparametric random effects models together. We propose to design efficient and stable algorithms for statistical inference and investigate the variation of the endocrine and metabolic functions for patients with different treatments. The research in this project will provide some scientific evidence for the clinical treatments and lay the theoretical foundation to improve the domestic level of treatment for the menopausal syndrome. In this project, we use variational and statistical image processing methods to extract key features needed in the following statistical analysis. Then, by combining the empirical likelihood and the EM algorithm, we will transform the nonparametric likelihood into profile likelihood under some constraint. And then，the Monte Carlo simulation method from the Bayesian statistics can be used in our models and this will avoid the numerical computation of high-dimensional integrals and asymptotic variance.

英文关键词： medical image analysis;random effects model;mixed model;Nonparametrical Bayes