Artificial Intelligence for Infectious Disease Imaging

传染病成像人工智能

• 批准号: 10682304
• 负责人: Joseph Frank
• 金额: --
• 依托单位: CLINICAL CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682304
• 关键词: 2019-nCoV; Architecture; Artificial Intelligence; COVID-19; Classification; Communicable Diseases; Communities; Data; Disease Progression; Ebola virus; Environment; Glass; High Performance Computing; Image; Infectious Diseases Research; Lassa virus; Lesion; Liver; Marburgvirus; Medical Imaging; Methods; Modeling; National Institute of Allergy and Infectious Disease; Nipah Virus; Organ; Pathology; Phenotype; Process; Research Personnel; SARS-CoV-2 infection; Severities; Severity of illness; Specificity; Spleen; Testing; Training; X-Ray Computed Tomography; base; classification algorithm; deep learning; experimental study; feature extraction; feature selection; imaging Segmentation; imaging study; interest; lung lesion; lung lobe; machine learning classification; machine learning method; medical countermeasure; multimodality; network models; nonhuman primate; novel; parallel processing; predictive modeling; radiomics; research facility

A CNN architecture was used to create a deep learning-based segmentation model of the liver seen on CT scans of a nonhuman primate (NHP). The CNN architecture was optimized by implementing a feature pyramid network (FPN). This FPN model was then utilized to generalize liver segmentation to other organs such as the spleen and then the whole lung and lung lesions in NHP models of SARS-CoV-2 infection, as conducted at the IRF. Lung lobe segmentation was developed in order to better correlate with histopathologic findings of COVID-19. This is a work-in-progress as the number of lung lobe annotations on CT scans is at a minimum due to the difficulty in creating this ground truth data. Currently, methods that function with low numbers of training data are being explored. To make these deep learning-based image segmentation methods available for use by the imaging community at the IRF, an automated pipeline process was developed. This pipeline utilizes the NIAID high performance computing environment (Locus) to allow for enhanced parallel processing with graphical processing units (GPUs). These methods are being used by other imaging researchers at IRF to post-process images acquired during infectious disease imaging studies of SARS-CoV-2, Marburg, Ebola, Nipah and Lassa viruses. In addition, these segmentations provide regions of interest for radiomic feature extraction. These hundreds of radiomic features have been input into feature selections methods, such as maximum relevance minimum redundancy (mRMR), to reduce and optimize features for predictive analyses. In addition, a novel feature selection method was developed (mRMR-permute), which uses permutation testing to automatically limit the number of features chosen. The predictive analyses are implemented with conventional machine learning methods that were explored to determine optimal use for SARS-CoV-2 imaging experiments. To further the specificity of image segmentations, a lesion phenotype classification algorithm is being developed. As an initial implementation, lung lesions seen in CT scans are to be classified as ground glass opacities or consolidations. This classification will help with severity assessment during longitudinal quantification.

CNN体系结构用于创建在非人类灵长类动物（NHP）CT扫描中看到的肝脏的深层分割模型。通过实现特征金字塔网络（FPN）来优化CNN体系结构。然后将此FPN模型用于将肝分割概括为其他器官，例如脾脏，然后在SARS-COV-2感染的NHP模型中的整个肺和肺部病变，如IRF所进行的。为了更好地与COVID-19的组织病理学发现相关，开发了肺叶分割。这是一个正在进行的工作，因为CT扫描上的肺叶注释的数量最少，因为难以创建此基础真实数据。当前，正在探索培训数据数量少的方法。 为了使这些基于学习的图像分割方法可用于IRF成像社区的使用，开发了自动化管道过程。该管道利用NIAID高性能计算环境（locus）允许使用图形处理单元（GPU）增强并行处理。 IRF的其他成像研究人员将这些方法用于SARS-COV-2，MARBURG，EBOLA，NIPAH和LASSA病毒的传染病成像研究中获得的后处理图像。此外，这些分割为放射素特征提取提供了感兴趣的区域。这些数百个放射线特征已被输入到特征选择方法中，例如最大相关性最小冗余（MRMR），以减少和优化预测分析的特征。此外，开发了一种新型的特征选择方法（MRMR-Permute），该方法使用排列测试自动限制所选功能的数量。预测分析是通过常规机器学习方法实施的，这些方法经过探讨，以确定SARS-COV-2成像实验的最佳用途。 为了进一步的图像分割的特异性，正在开发病变表型分类算法。作为最初的实施，在CT扫描中看到的肺部病变应归类为地面玻璃的不良度或合并。该分类将有助于在纵向定量期间进行严重评估。