Publication Details



Breast cancer is the most commonly diagnosed cancer in women worldwide. On a mammography, breast cancer can manifest as breast masses and/or subtle architectural distortions and/or microcalcifications. Among all the manifestations observed clinically, microcalcifications are usually considered a robust marker of an early breast cancer. Detecting, interpreting and discriminating the microcalcifications found in benign/malignant lesions, represents a challenge for clinicians considering the limitations that the current standard screening modalities used have nowadays (i.e.: lack of contrast, 2D/3D low resolution, superposition of tissue). An improved diagnostic accuracy of breast microcalcifications is of special importance for two main reasons: (a) to assess the likelihood of malignancy at the very initial phase of the disease (b) to avoid unnecessary invasive interventions. Despite the promising results that Computer Aided Detection and Diagnosis (CAD) systems have achieved over the past years, the existing systems provide diagnosis based on microcalcifications clusters visualized in 2D or low-resolution 3D. In contrast to the majority of the studies performed, in this PhD thesis we aim to provide breast cancer diagnosis based solely on individual microcalcification properties which are visualized in 3D and at high resolution. In vivo high-resolution breast imaging is currently not possible yet, so the images used are obtained by scanning breast biopsies with a micro-CT scanner. Several contributions were achieved in this thesis. As a first contribution we evaluated the feasibility of developing a machine learning CAD system able to diagnose breast cancer based only on handcrafted features of individual microcalcifications. As a second contribution, we explored for the first time the impact of image resolution (8µm, 16µm, 32µm, 64µm) when diagnosing individual breast microcalcifications. As a third contribution, we participated in a new data collection procedure and performed sensitivity analysis where we explored the effect of different segmentation thresholds in providing individual microcalcification diagnosis. As a fourth contribution, we evaluated the performance of a deep learning framework to provide benign/malignant diagnosis based on automatically learned features from high resolution 3D microcalcification images. Although our research is currently not directly applicable in vivo (3D high resolution in vivo breast imaging is still not possible), we demonstrated its potential to be used in further research/clinical scenarios as soon as further improvements in the current breast screening modalities will occur. At the moment, the results achieved can potentially be used in intra operative imaging to reduce the waiting time between tissue extraction and anatomopathological results. As a long term goal, with our study we aim to avoid unnecessary biopsies and considerably reduce costs for the healthcare system.