The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrast-enhanced) mammography images

Abstract

Characterization of microcalcification clusters in the breast and differentiation between benign and malignant structures on (contrast-enhanced) mammography (CEM) images is of great importance to determine cancerous lesions. Computer algorithms may help performing these tasks, but typically need large sets of data for model training. Therefore this paper develops a method to create synthetic microcalcification clusters that can later be used to overcome data sparsity problems. Starting from descriptors of the shape and size, both benign and malignant microcalcifications were created and then combined into 3-dimensional cluster models given realistic geometric properties. The distributions of the largest diameter and the number of microcalcifications per cluster in a set of 500 simulated clusters were set such that they agreed with those of real clusters. An existing simulation tool was then extended to insert the clusters into processed, low-energy CEM background images with appropriate contrast values. In a validation study comprised of 40 real and 40 synthetic cases, radiologists were asked to evaluate realism and malignancy. It was found that the shape and the structure of the individual microcalcifications as well as the complete clusters were realistic. Thus the descriptors were chosen correctly and enabled a good classification between benign and malignant cases. The realistic brightness and boundary smoothness proved the simulation tool can correctly insert the 3D clusters into real background images and is suitable of creating a large set of realistic microcalcification clusters simulated in existing (contrast-enhanced) mammography images. With improvements on the correspondence of insertion location in craniocaudal and mediolateral oblique view, which proved more challenging to simulate realistically, this promising method is expected to be applicable for modeling complete synthetic cases. Such a dataset can be used for data enrichment where data sources are limited and for development and training purposes.