UNASSIGNED: Sixteen nonexpert 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).
UNASSIGNED: The CNN-CADe improved the 3D search for the small microcalcification signal ( Δ AUC = 0.098 , p = 0.0002 ) and the 2D search for the large mass signal ( Δ AUC = 0.076 , p = 0.002 ). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D ( Δ Δ 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 ( Δ Δ AUC = 0.033 , p = 0.133 ).
UNASSIGNED: The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.
■16名非专家观察者通过数字乳房断层合成(DBT)体模和DBT体模的单个横截面切片进行了搜索。3D/2D搜索在有和没有基于卷积神经网络(CNN)的CADe支持系统的情况下发生。该模型为观察者提供了叠加在图像刺激上的边界框,同时他们寻找小的微钙化信号和大的质量信号。记录眼睛注视位置,并与ROC曲线下面积(AUC)的变化相关。
■CNN-CADe改进了对小的微钙化信号的3D搜索(ΔAUC=0.098,p=0.0002)和2D搜索大质量信号(ΔAUC=0.076,p=0.002)。对于小信号,3D中的CNN-CADe益处明显大于2D中的(ΔΔAUC=0.066,p=0.035)。对个体差异的分析表明,那些探索眼球运动最少的人从CNN-CADe中受益最多(r=-0.528,p=0.036)。然而,对于大信号,2D效益并不显著大于3D效益(ΔΔAUC=0.033,p=0.133)。
■CNN-CADe为小信号的3D(相对于2D)搜索带来了独特的性能优势,它减少了体积数据不足造成的误差。