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Proceedings Paper

Variability in the interpretation of mammograms: Do similar decisions entail similar visual sampling strategies?
Author(s): Claudia Mello-Thoms
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Paper Abstract

Eye position tracking has shown that, both in searching for lung nodules and in searching for breast cancer, most malignant lesions that are not reported do in fact attract the radiologists' visual attention, often for as long as cancers that are reported. Hence, detection is not the main problem for most radiologists; rather, image interpretation is the underlying reason why detected findings go unreported. According with models of image perception, the decision to report or to dismiss a perceived finding is made based upon not only on the conspicuity of the local elements but also on the selection of certain areas of the background parenchyma, which the radiologist uses to compare the finding against and thus determine its uniqueness. This sampling of the background corresponds to a visual search strategy. Nonetheless, several studies have shown that the final pattern observed in visual search is particular for each observer, and that even when the same observer searches the same image the pattern is likely to be different the second time around. This has led to the assumption that visual search is random. In this study we compare the visual sampling strategy of experienced mammographers as they search a case set of mammograms looking for benign and malignant masses. We determine whether similar decisions, that is, decisions to report the same findings, entail the sampling of similar areas of the background, regardless of in which order these areas were sampled.

Paper Details

Date Published: 17 March 2006
PDF: 8 pages
Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460R (17 March 2006); doi: 10.1117/12.646459
Show Author Affiliations
Claudia Mello-Thoms, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 6146:
Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Miguel P. Eckstein, Editor(s)

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