Comparative observer effects in 2D and 3D localization tasks.
Comparative observer effects in 2D and 3D localization tasks.
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DOI：
10.1117/1.jmi.8.4.041206
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Eckstein MP
中科院分区：
文献类型：
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作者：
Abbey CK;Lago MA;Eckstein MP
目的：三维“体积”成像方法现在是许多成像方式的医学成像的共同组成部分。对于人类观察者在浏览3D图像时如何定位被噪音和杂乱掩盖的目标，以及如何将其与局限于单个2D切片的类似任务进行比较，我们所知相对较少。方法：采用高斯随机纹理表示有噪声的体医学图像。受试者可以自由地查看图像，包括在搜索过程中滚动3D图像。总共评估了八种实验条件（2D与3D图像，大目标与小目标，幂律与白噪声）。我们使用任务效率和分类图像技术来分析这些实验的性能。结果：在3D任务中，中位反应时间大约是2D任务的9倍，错误试验的相对差异更大。效率数据显示，受试者在大目标的二维任务中表现出较高的统计效率，而在小目标的三维任务中表现出较高的统计效率。分类图像表明，这种分离背后的关键机制是无法整合多个切片以形成3D定位响应。三维分类图像的中心切片与相应的二维分类图像具有显著的相似性。结论：二维和三维任务在二维图像和三维图像中心切片之间表现出相似的权重模式。在3D任务中，切片之间的权重相对较小，导致相对于理想观察者的任务效率较低。
Purpose: Three-dimensional “volumetric” imaging methods are now a common component of medical imaging across many imaging modalities. Relatively little is known about how human observers localize targets masked by noise and clutter as they scroll through a 3D image and how it compares to a similar task confined to a single 2D slice. Approach: Gaussian random textures were used to represent noisy volumetric medical images. Subjects were able to freely inspect the images, including scrolling through 3D images as part of their search process. A total of eight experimental conditions were evaluated (2D versus 3D images, large versus small targets, power-law versus white noise). We analyze performance in these experiments using task efficiency and the classification image technique. Results: In 3D tasks, median response times were roughly nine times longer than 2D, with larger relative differences for incorrect trials. The efficiency data show a dissociation in which subjects perform with higher statistical efficiency in 2D tasks for large targets and higher efficiency in 3D tasks with small targets. The classification images suggest that a critical mechanism behind this dissociation is an inability to integrate across multiple slices to form a 3D localization response. The central slices of 3D classification images are remarkably similar to the corresponding 2D classification images. Conclusions: 2D and 3D tasks show similar weighting patterns between 2D images and the central slice of 3D images. There is relatively little weighting across slices in the 3D tasks, leading to lower task efficiency with respect to the ideal observer.