KEYWORDS: Signal detection, Computer aided detection, Digital breast tomosynthesis, 3D modeling, 3D image processing, Eye, Visualization, Education and training, Deep learning, Picture Archiving and Communication System
PurposeRadiologists 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.ApproachSixteen 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).ResultsThe 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 (ΔΔ 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 (ΔΔ AUC=0.033, p=0.133).ConclusionThe 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.
KEYWORDS: Liver, Computed tomography, Visual process modeling, Signal detection, Cancer detection, Image quality, Medical image visualization, Medical imaging, Visual information processing, Human vision
Objective assessment of medical image quality can be performed with mathematical model observers matched to radiologists. Foveated channelized Hotelling observer models (FCHO) have been shown to be more accurate predictors of the human search performance in simulated 3D images than standard model observers such as the ideal observer or the non-prewhitening observer with eye filter. However, nothing is known about the performance of FCHOs with the computed tomography (CT) modality as well as with images extracted from real patients. Patient-extracted images are smaller than simulated images and their size could be limiting for FCHOs as peripheral vision is modeled by an increasing spatial extent of channels. This study has two aims: to extend a foveated model observer to 2D anatomical liver CT images and to find channel parameters enabling the FCHO to match human performance. Regions of interest (ROIs) were automatically extracted from CT images of five patients’ livers and their size was of 100x100 pixels, a balance between the anatomical constraints and the modeling of peripheral vision. Two radiologist-validated small low-contrast hypodense hepatic metastases were simulated to generate signal-present ROIs. The signal diameters were of 1 cm relatively to the patient and their contrast of -50 HU. The foveated model observer used was a FCHO with dense difference-of-Gaussians channels that were optimized to the size of the extracted ROIs. The performance of the optimized FCHO could reproduce human performance for a detection task in anatomical liver CT images within standard error up to 9 degrees of visual angle. This study shows that optimized FCHOs could be used in more anthropomorphic assessments of image quality of CT units.
KEYWORDS: 3D image processing, Signal detection, Image processing, Digital breast tomosynthesis, Visualization, Linear filtering, Digital imaging, Diagnostics, 3D vision
For some imaging modalities (e.g., Digital Breast Tomosynthesis, DBT), radiologists are provided, in addition to the 3D image stack, a 2D image known as C-view, a synthesized image from the corresponding 3D slices. An understanding of the functional perceptual interaction between the 2D image and the 3D search remains unexplored. We have yet to elucidate the basic perceptual mechanisms of visual search and attention that drive possible added benefits of incorporating the C-View image in the diagnostic process. We explore how the presence of a 2D synthesized view influences the detectability of signals and eye movements during 3D search in 1/f2.8 filtered noise backgrounds. Six trained observers searched for a microcalcification-like signal and a mass-like signal in 3D volumes (100 slices) with or without an additional 2D synthesized image (2D-S). The 2D-S was obtained by applying a high pass filter and a pixelwise maximum operation across the slices. We found that the detection and localization of small microcalcification-like signals in the 3D images improves when presented together with the 2D-S (p < 0.01). For larger mass-like signals, there was an improvement but not to the same extent as the microcalcification. Additionally, search times are significantly shorter for both signals when the 3D volume is accompanied by the 2D-S versus when used alone (p < 0.05). Eye movement analysis showed significantly fewer search errors in the 2D-S + 3D condition relative to the 3D condition for the microcalcification (p < .001) but not for the mass. The results suggest that a 2D-S allows an observer to efficiently identify suspicious locations, guide the search in 3D, and mitigate detrimental effects of peripheral vision on the detectability of small signals.
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