PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
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.
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.
摘要：
■放射科医师的任务是在视觉上仔细检查由3D体积成像模式产生的大量数据。在3D搜索过程中，小信号可能会被忽视，因为它们很难在视觉外围检测到。机器学习和计算机视觉的最新进展导致了有效的计算机辅助检测（CADe）支持系统，具有减轻感知错误的潜力。
■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。
■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。
