Craniocaudal Mammograms Generation Using Image-to-Image Translation Techniques

Breast cancer is the leading cause of cancer death in women worldwide, emphasizing the need for prevention and early detection. Mammography screening plays a crucial role in secondary prevention, but large datasets of referred mammograms from hospital databases are hard to access due to privacy concerns, and publicly available datasets are often unreliable and unbalanced. We propose a novel workflow using a statistical generative model based on generative adversarial networks to generate high-resolution synthetic mammograms. Utilizing a unique 2D parametric model of the compressed breast in craniocaudal projection and image-to-image translation techniques, our approach allows full and precise control over breast features and the generation of both normal and tumor cases. Quality assessment was conducted through visual analysis, and statistical analysis using the first five statistical moments. Additionally a questionnaire was administered to 45 medical experts (radiologists and radiology residents). The results showed that the features of the real mammograms were accurately replicated in the synthetic ones, the image statistics overall correspond reasonably well, and the two groups of images were statistically indistinguishable in almost all cases according to the experts. The proposed workflow generates realistic synthetic mammograms with fine-tuned features. Synthetic mammograms are powerful tools that can create new or balance existing datasets, allowing for the training of machine learning and deep learning algorithms. These algorithms can then assist radiologists in tasks like classification and segmentation, improving diagnostic performance.