People searching for objects in 3D image stacks are less suc
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3-D imaging in particular has become popular because it provides a more complete picture of the target object and its context. In a study published in the journal, Current Biology the author said that We're actually worse at finding small targets in 3D image stacks than if they were in a single 2D image.

Researchers investigate 3D search for targets of various sizes in filtered noise and digital breast phantoms.

For a Bayesian ideal observer optimally processing the filtered noise and a convolutional neural network processing the digital breast phantoms, search with 3D image stacks increases target information and improves accuracy over the search with 2D images. In contrast, 3D search by humans leads to high miss rates for small targets easily detected in 2D search, but not for larger targets more visible in the visual periphery.

Analyses of human eye movements, perceptual judgments, and a computational model with a foveated visual system suggest that human errors can be explained by interaction among a target’s peripheral visibility, eye movement under-exploration of the 3D images, and a perceived overestimation of the explored area. Instructing observers to extend the search reduces 75% of the small target misses without increasing false positives. Results with twelve radiologists confirm that even medical professionals reading realistic breast phantoms have high miss rates for small targets in 3D search.

Thus, under-exploration represents a fundamental limitation to the efficacy with which humans search in 3D image stacks and miss targets with these prevalent image technologies.

Source: https://doi.org/10.1016/j.cub.2020.12.029
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