IEEE Trans Vis Comput Graph. 2017 Jan;23(1):311-320. doi: 10.1109/TVCG.2016.2598796.
We present an interactive visual analytics framework, GazeDx (abbr. of GazeDiagnosis), for the comparative analysis of gaze data from multiple readers examining volumetric images while integrating important contextual information with the gaze data. Gaze pattern comparison is essential to understanding how radiologists examine medical images, and to identifying factors influencing the examination. Most prior work depended upon comparisons with manually juxtaposed static images of gaze tracking results. Comparative gaze analysis with volumetric images is more challenging due to the additional cognitive load on 3D perception. A recent study proposed a visualization design based on direct volume rendering (DVR) for visualizing gaze patterns in volumetric images; however, effective and comprehensive gaze pattern comparison is still challenging due to a lack of interactive visualization tools for comparative gaze analysis. We take the challenge with GazeDx while integrating crucial contextual information such as pupil size and windowing into the analysis process for more in-depth and ecologically valid findings. Among the interactive visualization components in GazeDx, a context-embedded interactive scatterplot is especially designed to help users examine abstract gaze data in diverse contexts by embedding medical imaging representations well known to radiologists in it. We present the results from two case studies with two experienced radiologists, where they compared the gaze patterns of 14 radiologists reading two patients' volumetric CT images.
我们提出了一个交互式视觉分析框架 GazeDx(凝视诊断的缩写),用于比较分析多个读者在检查容积图像时的注视数据,同时将重要的上下文信息与注视数据集成。注视模式比较对于理解放射科医生如何检查医学图像以及确定影响检查的因素至关重要。大多数先前的工作都依赖于与注视跟踪结果的手动并列静态图像进行比较。由于 3D 感知的额外认知负担,使用容积图像进行比较注视分析更具挑战性。最近的一项研究提出了一种基于直接体绘制(DVR)的可视化设计,用于可视化容积图像中的注视模式;然而,由于缺乏用于比较注视分析的交互式可视化工具,有效的和全面的注视模式比较仍然具有挑战性。我们通过将关键上下文信息(如瞳孔大小和窗口)集成到分析过程中,同时利用 GazeDx 来应对这一挑战,以获得更深入和更符合实际情况的发现。在 GazeDx 的交互式可视化组件中,一个上下文嵌入的交互式散点图是专门设计的,通过将放射科医生熟知的医学成像表示嵌入其中,帮助用户在不同的上下文中检查抽象的注视数据。我们提出了两个有经验的放射科医生进行的两项案例研究的结果,他们比较了 14 名放射科医生阅读两名患者容积 CT 图像的注视模式。