Computational method to artificially insert clusters of microcalcifications in digital breast tomosynthesis

Abstract

Digital Breast Tomosynthesis (DBT) is a medical imaging modality that has been increasingly used for breast cancer screening. To improve the accuracy in the early detection of breast cancer, it is common to use tools based on image processing to improve the quality of DBT images and, consequently, the visibility of lesions of clinical interest. Microcalcification (MC) clusters are in the class of findings that may indicate the early stages of breast cancer. To evaluate the impact of image processing methods in the detection of breast lesions, human perception studies are usually performed, where detection accuracy is evaluated using positive and negative cases, i.e., images with and without lesions. However, only a small fraction of clinical cases have positive cases and the exact location of the lesion is not always known. Thus, in this work, we present a method to artificially insert MC cluster in normal exams of DBT. A set of MCs was segmented from clinical cases acquired in a prone stereotactic breast biopsy system. In the pipeline, we built a MC cluster and insert it in a 3D volume. Then, we project it on the detector and inserted the MC cluster into the projections of a clinical DBT exam. It is possible to define the size, contrast, and location of the MC cluster accordingly. We performed a two-alternative forced-choice (2-AFC) study with six experienced medical physicists and the average success rate was 53.83%, suggesting that readers, on average, could not distinguish between real and simulated MCs. Overall results showed that images with simulated MC are similar to the real clinical cases. Our source code is available at .