Several clinical image databases are currently available to support scientific research in the medical field. These images are generally used to validate studies based on measuring the sensitivity and specificity of a particular clinical task. In the case of digital mammography, the radiation dose directly influences the quality of the image and consequently the performance of radiologists. Therefore, it is important to conduct studies to find a balance between image quality and radiation dose. Image processing methods are typically employed to optimize this relationship. For the evaluation of these methods, it is crucial to have a mammographic image database with specific characteristics, currently unavailable for scientific use. For example, this image database should contain sets of images from the same patient acquired at different radiation doses with breast lesions in known locations. This is achievable using computational methods for noise and microcalcification insertion into pre-acquired clinical images. In this context, the present work aims to present a cloud-based application for on-demand generation of a clinical mammographic image database with different radiation doses and breast lesions. From a set of pre-acquired clinical digital mammograms, it is possible to create N databases with different characteristics. This technique can also be considered as data augmentation.
KEYWORDS: Digital breast tomosynthesis, Medical imaging, Computer simulations, Modulation transfer functions, Breast cancer, Voxels, Image processing, Cancer detection, Breast
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 www.github.com/LAVI-USP/MCInsertionPackage-DBT.
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