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Mammography is the primary imaging modality used for early detection anddiagnosis of breast cancer. X-ray mammogram analysis mainly refers to thelocalization of suspicious regions of interest followed by segmentation,towards further lesion classification into benign versus malignant. Amongdiverse types of breast abnormalities, masses are the most important clinicalfindings of breast carcinomas. However, manually segmenting breast masses fromnative mammograms is time-consuming and error-prone. Therefore, an integratedcomputer-aided diagnosis system is required to assist clinicians for automaticand precise breast mass delineation. In this work, we present a two-stagemulti-scale pipeline that provides accurate mass contours from high-resolutionfull mammograms. First, we propose an extended deep detector integrating amulti-scale fusion strategy for automated mass localization. Second, aconvolutional encoder-decoder network using nested and dense skip connectionsis employed to fine-delineate candidate masses. Unlike most previous studiesbased on segmentation from regions, our framework handles mass segmentationfrom native full mammograms without any user intervention. Trained on INbreastand DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of80.44% on INbreast test images, outperforming state-of-the-art. Our systemshows promising accuracy as an automatic full-image mass segmentation system.Extensive experiments reveals robustness against the diversity of size, shapeand appearance of breast masses, towards better interaction-free computer-aideddiagnosis.

Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention