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Proceedings Paper

Breast screening: visual search as an aid for digital mammographic interpretation training
Author(s): Yan Chen; Ann Turnbull; Jonathan James; Alastair Gale; Hazel Scott
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Paper Abstract

Digital mammography is gradually being introduced across all breast screening centres in the UK during 2010. This provides increased training opportunities using lower resolution, lower cost and more widely available devices, in addition to the clinical digital mammography workstations. This study examined how experienced breast screening personnel performed when they examined sets of difficult DICOM two-view screening cases in three conditions: on GE digital mammography workstations, on a standard LCD monitor (using a DICOM viewer) and an iPhone (running Osirix software). In each condition they either viewed the full images unaided or were permitted to use the post-processing manipulations of pan, zoom and window level/width adjustments. For each case they had to report the feature type, rate their confidence on the presence of abnormality, classify the case and specify case density. Their visual search behaviour was recorded throughout using a head mounted eye tracker. Additionally aspects of their real life screening performance and performance on a national self assessment scheme were examined. Data indicate that screening experience plays a major role in doing well on the self assessment scheme. Task performance was best on the clinical workstation. However, the data also suggest that a DICOM viewer that runs on a PC or laptop with a standard LCD display allows viewing digital images in full resolution support impressive cancer detection performance. The iPhone is not ideal for examining full images due to the amount of scrolling and zooming required. Overall, the results indicate that low cost devices could be used to provide additional tailored training as long as device resolution and HCI aspects are carefully considered.

Paper Details

Date Published: 23 February 2010
PDF: 11 pages
Proc. SPIE 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, 76270C (23 February 2010); doi: 10.1117/12.843820
Show Author Affiliations
Yan Chen, Loughborough Univ. (United Kingdom)
Ann Turnbull, Derby Breast Screening Ctr. (United Kingdom)
Jonathan James, Nottingham Breast Institute (United Kingdom)
Alastair Gale, Loughborough Univ. (United Kingdom)
Hazel Scott, Loughborough Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 7627:
Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment
David J. Manning; Craig K. Abbey, Editor(s)

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