Two-Stage Multi-Scale Mass Segmentation From Full Mammograms
Yutong Yan, Pierre-Henri Conze, Gwenolé Quellec, Mathieu Lamard, Béatrice Cochener, Gouenou Coatrieux · 2021
Manually segmenting masses from native mammograms is a very time-consuming and error-prone task. Therefore, an integrated computer-aided diagnosis (CAD) system is required to assist radiologists for automatic and precise breast mass delineation. In this work, we present a two-stage multi-scale pipeline that provides accurate mass delineations from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is used to fine-delineate candidate masses. Experiments on public DDSM-CBIS and INbreast datasets reveals strong robustness against the diversity of size, shape and appearance of masses, with an average Dice of 80.44% on INbreast. This shows promising accuracy as an automated full-image mass segmentation system, towards better interaction-free CAD.