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: 7636755
• 负责人: DEV P CHAKRABORTY
• 金额: $ 37.06万
• 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/7636755
• 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; Perception; Performance; Poisson Distribution; Positioning Attribute; Probability; Receiver Operating Characteristics; Reporting; Signal Transduction; Site; Testing; Time; Training; Validation; base; gaze; improved; indexing; novel strategies; public health relevance; 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）：眼睛跟踪方法是研究医学形象感知的基础，并且是关于放射科医生解释性失败的发现的核心。但是，对眼睛跟踪数据的分析相当原始，并且忽略了潜在的有价值的信息。我们提出了一种基于最近开发的模型来分析接收器操作特征（ROC）数据，自由响应操作特征（FROC）标记数据和位置特定ROC（LROC）数据的新方法。基于Chakraborty的模型，我们提出了一种整合眼睛位置数据的真实和假阳性特征的分析的方法，并利用了放射科医生的怀疑程度表明可疑区域。预计将FROC性能数据包含在对图像感知中的理解比单独进行的轨迹更好。综合分析是通过最近的两个进展来实现的：Chakraborty搜索模型和一种估计其参数的方法。该模型的参数对应于在眼睛跟踪研究中测量的物理量。该项目包括量化这些对应关系，提供了综合分析的演示，并显示了其优于单独的眼睛跟踪的优势。到目前为止，目前的研究和FROC研究已经沿着独立的轨道进行，一个是了解图像感知的，另一个是为了衡量性能。该项目显示了合并的方法如何产生更强大的工具，用于分析眼睛跟踪数据并理解图像感知。通过更好地理解基于改进分析的图像感知，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生培训和改进的CAD算法。在我们项目结束时，将免费获得丰富的数据集，并同时获得了Froc和眼睛位置数据。公共卫生相关性吸引人的设备措施是放射科医生所看的。这些信息对于了解医学形象感知和学习为什么会发生放射科医生解释性失败至关重要。但是，对此类数据的分析是原始的，并且忽略了有价值的信息。我们提出了一种新的方法来分析眼神跟踪数据。通过更好地了解图像感知，我们将更好地改善放射科医生的绩效并减少解释性错误。