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A 2-AFC study to validate artificially inserted microcalcification clusters in digital mammography
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Paper Abstract

The validation of many studies in the field of medical imaging relies on the measurement of sensitivity and specificity of a given task. In breast cancer screening, it is common to validate studies by assessing the impact to the sensitivity and specificity of cancer detection through experiments such as N-alternative forced-choice (N-AFC) or receiver operating characteristics (ROC). In general, these experiments require large datasets of clinical mammograms containing a considerable number of cases with true positive findings. Nonetheless, in clinical practice only a small fraction of the patients (<1%) are actually diagnosed with breast cancer. One common approach to solve this data constraint is to insert lesions to images taken from healthy patients, thus creating a hybrid dataset with real and artificial data. In this work we investigate a simple method to perform the segmentation of microcalcification clusters from clinical cases, followed by the artificial insertion of the clusters into normal (true negative) mammograms. A 2-AFC human observers study was performed, where observers were asked to choose between artificially inserted and real clusters of microcalcifications. Artificially inserted clusters were selected 47.3% of the time, indicating that the readers were not able to distinguish between artificially inserted and real clusters (p = 0.65). A dataset containing 100 BI-RADS 1 clinical mammograms with artificially inserted clusters was visually evaluated by an experienced radiologist, who was asked to comment on the appearance and positioning of the clusters. The reader considered the appearance and location realistic in 98% of the images. Two cases had problems related to patient motion and breast segmentation. The codes and segmented lesions are available for download at: https://github.com/LAVI-USP/MCInsertionPackage.

Paper Details

Date Published: 4 March 2019
PDF: 7 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520R (4 March 2019); doi: 10.1117/12.2513031
Show Author Affiliations
Lucas R. Borges, Ribeirão Preto Medical School (Brazil)
Tampere Univ. (Finland)
Escola de Engenharia de São Carlos (Brazil)
Paulo M. de Azevedo Marques, Ribeirão Preto Medical School (Brazil)
Marcelo A. C. Vieira, Escola de Engenharia de São Carlos (Brazil)


Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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