Assessment of prostate cancer detection with a visual-search human model observer

Anando Sen; Faraz Kalantari; Howard C. Gifford

doi:10.1117/12.2043743

11 March 2014 Assessment of prostate cancer detection with a visual-search human model observer

Anando Sen, Faraz Kalantari, Howard C. Gifford

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90370Q (2014) https://doi.org/10.1117/12.2043743
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Early staging of prostate cancer (PC) is a significant challenge, in part because of the small tumor sizes in- volved. Our long-term goal is to determine realistic diagnostic task performance benchmarks for standard PC imaging with single photon emission computed tomography (SPECT). This paper reports on a localization receiver operator characteristic (LROC) validation study comparing human and model observers. The study made use of a digital anthropomorphic phantom and one-cm tumors within the prostate and pelvic lymph nodes. Uptake values were consistent with data obtained from clinical In-111 ProstaScint scans. The SPECT simulation modeled a parallel-hole imaging geometry with medium-energy collimators. Nonuniform attenua- tion and distance-dependent detector response were accounted for both in the imaging and the ordered-subset expectation-maximization (OSEM) iterative reconstruction. The observer study made use of 2D slices extracted from reconstructed volumes. All observers were informed about the prostate and nodal locations in an image. Iteration number and the level of postreconstruction smoothing were study parameters. The results show that a visual-search (VS) model observer correlates better with the average detection performance of human observers than does a scanning channelized nonprewhitening (CNPW) model observer.

Citation Download Citation

Anando Sen, Faraz Kalantari, and Howard C. Gifford "Assessment of prostate cancer detection with a visual-search human model observer", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370Q (11 March 2014); https://doi.org/10.1117/12.2043743

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