New Methods for Analysis of Eye-tracking Data for Medical Image Perception Resear

医学图像感知研究眼动追踪数据分析新方法

• 批准号: 7504345
• 负责人: DEV P CHAKRABORTY
• 金额: $ 37.99万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7504345
• 关键词: Algorithms; Anatomy; Binomial Distribution; Characteristics; Chest; Classification; Computer Assisted; Computer software; Data; Data Analyses; Data Set; Databases; Decision Making; Detection; Development; Diagnostic; Eye; Failure; Image; Learning; Lesion; Location; Lung nodule; Malignant Neoplasms; Mammography; Measures; Medical Imaging; Methods; Modeling; Noise; Numbers; Other Finding; Perception; Performance; Poisson Distribution; Positioning Attribute; Probability; Public Health; Rate; Receiver Operating Characteristics; Reporting; Signal Transduction; Site; Statistically Significant; Testing; Time; Training; Validation; base; gaze; improved; indexing; novel strategies; radiologist; response; sample fixation; study characteristics; success; tool

DESCRIPTION (provided by applicant): Eye-tracking methods are fundamental for the study of medical image perception and central to discoveries about why radiologist interpretative failures occur. However, analysis of eye-tracking data has been rather primitive and ignores potentially valuable information. We propose a new approach to the analysis of eye-tracking data based on a recently developed model for the analysis of receiver operating characteristic (ROC) data, free-response operating characteristic (FROC) mark-rating data, and location- specific ROC (LROC) data. Based on Chakraborty's model, we propose a method that integrates the analysis of true and false positive characterizations of eye position data and uses the degree of suspicion of radiologist indicated suspicious regions. The inclusion of the FROC performance data is expected to yield better understanding of image perception than is possible via eye-tracking alone. The integrated analysis is enabled by two recent developments: the Chakraborty search model and a method for estimating its parameters. The parameters of the model correspond to physical quantities that are measured in eye-tracking studies. The project consists of quantifying these correspondences, providing a demonstration of integrated analysis, and showing its advantages over eye-tracking alone. Eye-tracking and FROC studies have so far proceeded along independent tracks, one to understand image perception and the other to measure performance. This project shows how a combined approach can yield a more powerful tool for analyzing eye-tracking data and understanding image perception. With better understanding of image perception based on improved analysis, we will be better able to improve diagnostic performance. Applications of this method include radiologist training and improved CAD algorithms. The rich dataset of simultaneously acquired FROC and eye-position data, and analysis software will be made freely available at the close of our project. PUBLIC HEALTH RELEVANCE Eye-tracking apparatus measures where radiologists look. This information is fundamental to understand medical image perception and central to learning why radiologist interpretative failures occur. However, analysis of such data has been primitive and ignores valuable information. We propose a new approach to the analysis of eye-tracking data. With better understanding of image perception we will be better able to improve radiologist performance and reduce interpretive errors.

描述（由申请人提供）：眼动跟踪方法是医学图像感知研究的基础，也是发现放射科医生解释失败原因的关键。然而，对眼动追踪数据的分析相当原始，忽略了潜在的有价值的信息。我们提出了一种新的方法来分析眼动跟踪数据的基础上，最近开发的模型，用于分析的受试者工作特性（ROC）数据，自由反应工作特性（FROC）标记评级数据，和位置特定的ROC（LROC）数据。基于Chakraborty的模型，我们提出了一种方法，该方法集成了对眼睛位置数据的真阳性和假阳性特征的分析，并使用放射科医生指示的可疑区域的怀疑程度。包括FROC性能数据预计将产生更好的理解图像感知比可能通过眼睛跟踪单独。综合分析是由两个最近的事态发展：Chakraborty搜索模型和估计其参数的方法。模型的参数对应于在眼动跟踪研究中测量的物理量。该项目包括量化这些对应关系，提供综合分析的演示，并显示其优于单独的眼动跟踪。眼动追踪和FROC研究到目前为止都是沿着沿着独立的轨道进行的，一个是理解图像感知，另一个是测量表现。该项目展示了一种组合方法如何产生一种更强大的工具，用于分析眼动跟踪数据和理解图像感知。通过基于改进的分析更好地理解图像感知，我们将能够更好地提高诊断性能。该方法的应用包括放射科医师培训和改进的CAD算法。同时获得的FROC和眼位数据的丰富数据集以及分析软件将在我们的项目结束时免费提供。眼动追踪仪测量放射科医生的视线范围。这些信息是理解医学图像感知的基础，也是了解放射科医生解释失败原因的核心。然而，对这些数据的分析是原始的，忽略了有价值的信息。我们提出了一种新的方法来分析眼动跟踪数据。随着对图像感知的更好理解，我们将能够更好地提高放射科医生的表现并减少解释错误。