Fine-Grained Spatial Information Extraction For Radiology Reports

放射学报告的细粒度空间信息提取

• 批准号: 10288320
• 负责人: Kirk Edward Roberts
• 金额: $ 26.96万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288320
• 关键词: Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer' s disease risk; Anatomy; Architecture; Artificial Intelligence; Atrophic; Brain; Classification; Clinical Data; Complex; Data; Data Set; Diagnosis; Diffuse; Dilatation - action; Disease; Goals; Grain; Horns; Image; Information Retrieval; Knowledge; Label; Linguistics; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Medial; Medical Imaging; Methods; Natural Language Processing; Parents; Performance; Phenotype; Positron-Emission Tomography; Radiology Specialty; Reporting; Research; Resources; Roentgen Rays; Temporal Lobe; Text; Training; Validation; base; deep learning; disease phenotype; image processing; imaging study; improved; machine learning method; neuroimaging; parent project; precision medicine; radiological imaging; risk prediction; scale up; spatial relationship

ABSTRACT Automated medical image classification has seen enormous performance improvements recently, particularly in radiology. The application of these approaches to Alzheimer's Disease (AD), however, has been limited due to relatively small datasets and the limited granularity of their corresponding phenotypes. The dataset size issue is problematic as the machine learning (ML) methods that have achieved such remarkable performance often require enormous amounts of labeled data for training. Furthermore, the phenotype granularity issue impedes the targeted studying of AD along the lines of what is seen in the “precision medicine” approaches to diseases such as cancer. Solutions exist, however, as an increasingly accepted means of acquiring large amounts of labeled data is through the use of natural language processing (NLP) on the free-text reports associated with an image If a radiology report describes a patient's AD-related finding, the associated image(s) can be used to train an image classifier. The parent project to this supplemental proposal (R21EB029575) proposes just such a NLP method while simultaneously solving the granularity issue by extracting fine-grained spatial information from the report. In the parent project, we are developing NLP resources and methods to improve the automated labeling of radiology images using the corresponding study reports. The parent is not specific to AD (or any disease), so this supplement will enable us to focus on this particularly important disease, which will benefit significantly from improved ML-based imaging. We will focus on MRI and PET scans. The Aims here parallel the parent project, each focusing on methods that specifically improve NLP for AD radiological indicator extraction as well as the validation of image classification from the corresponding labels. These Aims include (1) extending the spatial representation and corpus for Alzheimer's, (2) extending the NLP methods for automatic extraction, and (3) validating the AD-related labels for use in image classification. The long-term impact of this project is to substantially improve AD diagnosis by scaling up the amount of labeled data available to ML-based classifiers. The short-term goal supplement is to focus our NLP/Imaging combination research on the complex task of improving AD diagnosis. By extending our project with a specific target for AD, we will initiate a sizable research effort toward this goal.

抽象的 自动医学图像分类最近取得了巨大的性能改进， 特别是在放射学方面。然而，这些方法在阿尔茨海默病 (AD) 中的应用 由于数据集相对较小及其相应的粒度有限，因此受到限制 表型。数据集大小问题是有问题的，因为机器学习 (ML) 方法具有 取得如此出色的性能往往需要大量的标记数据进行训练。 此外，表型粒度问题阻碍了 AD 的针对性研究 在针对癌症等疾病的“精准医学”方法中可以看到什么。然而，解决方案是存在的 作为一种越来越被接受的获取大量标记数据的方法是通过使用 对与图像相关的自由文本报告进行自然语言处理 (NLP) 如果放射学报告 如果描述了患者的 AD 相关发现，则相关图像可用于训练图像分类器。 该补充提案 (R21EB029575) 的父项目提出了这样一种 NLP 方法，同时 通过从数据中提取细粒度的空间信息，同时解决粒度问题 报告。在父项目中，我们正在开发 NLP 资源和方法来提高自动化 使用相应的研究报告标记放射学图像。父级不特定于 AD（或 任何疾病），因此这种补充剂将使我们能够专注于这种特别重要的疾病，这将 基于 ML 的成像改进显着受益。我们将重点关注 MRI 和 PET 扫描。目标 这里与父项目平行，每个项目都专注于专门改进 AD 的 NLP 的方法 放射指标提取以及相应的图像分类验证 标签。 这些目标包括 (1) 扩展阿尔茨海默病的空间表示和语料库，(2) 扩展 用于自动提取的 NLP 方法，以及 (3) 验证图像中使用的 AD 相关标签 分类。 该项目的长期影响是通过扩大 AD 诊断的数量来大幅改善 AD 诊断。 可用于基于 ML 的分类器的标记数据。短期目标补充是让我们集中精力 NLP/影像结合研究改善 AD 诊断的复杂任务。通过扩展我们的 一个针对 AD 的特定目标的项目，我们将为此目标启动大量研究工作。