Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3d search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors. Approach: Sixteen non-expert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC). Results: The CNN-CADe improved the 3D search for the small microcalcification signal (delta AUC = 0.098, p = 0.0002) and the 2D search for the large mass signal (delta AUC = 0.076, p = 0.002). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D (delta delta AUC = 0.066, p = 0.035). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe (r = -0.528, p = 0.036). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit (delta delta AUC = 0.033, p = 0.133). Conclusion: The CNN-CADe brings unique performance benefits to the 3D (vs. 2D) search of small signals by reducing errors caused by the under-exploration of the volumetric data.

翻译：目的：放射科医生需要视觉检查三维容积成像模态产生的大量数据。在三维搜索过程中，小信号因难以在视觉周边区域被察觉而易被忽略。机器学习和计算机视觉领域的最新进展催生了有效的计算机辅助检测（CADe）支持系统，有潜力减少感知错误。方法：16名非专业观察者搜索了数字乳腺断层合成（DBT）体模及其单层横截面切片。三维/二维搜索分别在有无基于卷积神经网络（CNN）的CADe支持系统条件下进行。该模型在观察者寻找微小钙化信号和大肿块信号时，为其提供叠加在图像刺激上的边界框。记录眼动注视位置，并将其与受试者工作特征曲线下面积（AUC）的变化进行关联分析。结果：CNN-CADe改善了三维搜索中微小钙化信号的检出（ΔAUC=0.098，p=0.0002）以及二维搜索中大肿块信号的检出（ΔAUC=0.076，p=0.002）。CNN-CADe在三维场景中对小信号的提升效果显著优于二维场景（ΔΔAUC=0.066，p=0.035）。个体差异分析表明，眼动探索最少的观察者从CNN-CADe中获益最大（r=-0.528，p=0.036）。然而对于大信号，二维场景下的提升效果并未显著优于三维场景（ΔΔAUC=0.033，p=0.133）。结论：CNN-CADe通过减少因容积数据探索不足造成的错误，在小信号的三维（相较于二维）搜索中展现出独特的性能优势。