ArticleProceedings of SPIE--the International Society for Optical Engineering2017
The role of extra-foveal processing in 3D imaging.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- A 2D Synthesized Image Improves the 3D Search for Foveated Visual Systems.IEEE transactions on medical imaging · 2023Article
- Mandating Limits on Workload, Duty, and Speed in Radiology.Radiology · 2022Review
- Medical image quality metrics for foveated model observers.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Comparative observer effects in 2D and 3D localization tasks.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Foveated Model Observers for Visual Search in 3D Medical Images.IEEE transactions on medical imaging · 2021Article
- What do radiologists look for? Advances and limitations of perceptual learning in radiologic search.Journal of vision · 2020Review
- Search of low-contrast liver lesions in abdominal CT: the importance of scrolling behavior.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Measurement of the useful field of view for single slices of different imaging modalities and targets.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Evaluation of Convolutional Neural Networks for Search in 1/fProceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Analysis of Perceptual Expertise in Radiology - Current Knowledge and a New Perspective.Frontiers in human neuroscience · 2019Review
- Interactions of lesion detectability and size across single-slice DBT and 3D DBT.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Evaluation of Search Strategies for Microcalcifications and Masses in 3D Images.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.