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Proceedings Paper

Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms
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Paper Abstract

Model observers for image quality assessment have been extensively used in the field of medical imaging. The majority of model observer developments have involved signal detection tasks with a few number of signal locations and models that have not explicitly incorporated the varying resolution in visual processing across the visual field (foveated vision). Here, we evaluate search performance by human and model observers in 3D search and 2D single-slice search with DBT virtual phantoms images for a simulated single simulated macrocalcification. We compare the ability of a Channelized Hotelling Observer model (CHO) and a Foveated Channelized Hotelling model (FCHO) in predicting human performance across 2D and 3D search. Human performance detecting the macrocalcification signal was significantly higher in 2D than in 3D (proportion correct, PC = 0.89 vs 0.68). However, the CHO model predicted a lower performance in 2D than in 3D search (PC = 0.84 vs 0.93). The FCHO, that processes the visual field with lowering spatial detail as the distance increases from the point of fixation, executes eye movements, and scrolls across slices, correctly predicts the relative performance for the detection of the macrocalcification in 2D and 3D search (PC = 0.92 vs 0.59). These results suggest that foveation is a key component for model observers when predicting human performance detecting small signals in DBT search.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160V (16 March 2020); doi: 10.1117/12.2550485
Show Author Affiliations
Miguel A. Lago, Univ. of California, Santa Barbara (United States)
Bruno Barufaldi, Univ. of Pennsylvania (United States)
Predrag R. Bakic, Univ. of Pennsylvania (United States)
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Andrew D. Maidment, Univ. of Pennsylvania (United States)
Miguel P. Eckstein, Univ. of California, Santa Barbara (United States)


Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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