  Mammography is the primary imaging modality used for early detection and
diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the
localization of suspicious regions of interest followed by segmentation,
towards further lesion classification into benign versus malignant. Among
diverse types of breast abnormalities, masses are the most important clinical
findings of breast carcinomas. However, manually segmenting breast masses from
native mammograms is time-consuming and error-prone. Therefore, an integrated
computer-aided diagnosis system is required to assist clinicians for automatic
and precise breast mass delineation. In this work, we present a two-stage
multi-scale pipeline that provides accurate mass contours 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 employed to fine-delineate candidate masses. Unlike most previous studies
based on segmentation from regions, our framework handles mass segmentation
from native full mammograms without any user intervention. Trained on INbreast
and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of
80.44% on INbreast test images, outperforming state-of-the-art. Our system
shows promising accuracy as an automatic full-image mass segmentation system.
Extensive experiments reveals robustness against the diversity of size, shape
and appearance of breast masses, towards better interaction-free computer-aided
diagnosis.
