Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets

Lago, Miguel A.; Jonnalagadda, Aditya; Abbey, Craig K.; Barufaldi, Bruno; Bakić, Predrag R.; Maidment, Andrew D. A.; Leung, Winifred K.; Weinstein, Susan P.; Englander, Brian S.; Eckstein, Miguel P.

doi:10.1016/j.cub.2020.12.029

Cited by 19 publications

(22 citation statements)

References 75 publications

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“…This finding would appear to be at odds with recent studies by Lago, Eckstein, and colleagues, 33 , 58 – 60 demonstrating substantial performance reductions for small targets in 3D search tasks. However, it is important to note a fundamental difference between those experiments and the results reported here.…”

Section: Discussionmentioning

confidence: 58%

“… 24 – 27 All of these works have used 2D simulated images to evaluate properties of human observers. There have been far fewer studies comparing and modeling observer effects between 2D and 3D images, with some notable examples 28 – 33 nonetheless, which makes the simulated-image approach more appealing for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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Comparative observer effects in 2D and 3D localization tasks

2021

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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.

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Section: Discussionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Comparative observer effects in 2D and 3D localization tasks

2021

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“…A similar pattern of results has been seen in lung cancer screening with the move from two-dimensional chest Xrays to three-dimensional computerized tomography (CT) scans. In this issue of Current Biology, Lago et al 4 show that the move from two dimensions to three dimensions is not a guarantee of improved detection. To oversimplify somewhat, breast cancer screening can be thought of as a search for relatively big, low contrast masses and relatively small, high contrast calcifications.…”

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confidence: 99%

“…To oversimplify somewhat, breast cancer screening can be thought of as a search for relatively big, low contrast masses and relatively small, high contrast calcifications. In experiments with simulated breast imagery, Lago et al 4 found that detection of big targets was improved in the three-dimensional stack, but detection of small targets actually got worse. This is interesting because their modeling work shows that performance should have been better in the three-dimensional stack if the humans were using the information optimally.…”

mentioning

confidence: 99%

“…Thus, if you look at this 'X', you can see that there is what appears to be writing to the left and right of the X, but you can read only the letters in a narrow range around fixation. Lago et al 4 tracked the eye movements and fixations of their observers and offer a biologically plausible model that shows that those observers did not look at enough of the three-dimensional image to reliably find small targets. Importantly, when asked, observers overestimated how much of the three-dimensional volume of imagery they had examined.…”

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confidence: 99%

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Visual Perception: How Better Imaging Can Make Things Worse

Wolfe

2021

Current Biology

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Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

et al. 2022

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Purpose Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. Methods Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I–IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II–IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. Results The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. Conclusions For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs.

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Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets

Cited by 19 publications

References 75 publications

Comparative observer effects in 2D and 3D localization tasks

Comparative observer effects in 2D and 3D localization tasks

Visual Perception: How Better Imaging Can Make Things Worse

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

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