Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors

Abstract

Segmentation of digital mammograms (DMs) into background, breast, and pectoral muscle is an important pre-processing step for many medical imaging pipelines. Our aim is to propose a segmentation method suited for processed DMs that generalizes across cranio-caudal (CC) and medio-lateral oblique (MLO) projections, and across models of different vendors. A dataset of 247 diagnostic DM exams was used, totaling 493 CC and 494 MLO processed images, of which 199 (40.4%) and 486 (98.4%) contained a pectoral muscle, respectively. The images were acquired with 10 different DM models from GE (73%) and Siemens (27%). The multi-class segmentation was done by a U-Net trained with a multi-class weighted focal loss. Several types of data augmentation were used during training, to generalize across model types, including random look-up table and random elastic and gamma transformations. The DICE coefficients for the segmentations were (mean ± std. dev.) 0.995 ± 0.005, 0.980 ± 0.016, 0.839 ± 0.243 for background, breast, and pectoral muscle, respectively. Background segmentation did not differ significantly between CC and MLO images. The pectoral muscle segmentation resulted in a higher DICE coefficient for MLO (0.932 ± 0.104) than CC images (0.636 ± 0.323). The false positive rate of pectoral muscle segmentation was 1.5% in CC images without any pectoral muscle. Among different model types, the mean overall DICE coefficients ranged from 0.985-0.990 for the different system models. The developed method yielded accurate overall segmentation results, independent of view, and was able to generalize well over mammograms acquired by systems of different vendors.