Exploring Deep-Based Approaches for Semantic Segmentation of Mammographic Images


The success of CAD techniques for the detection of abnormalities in mammographic images depends on an accurate differentiation between pectoral muscle and breast tissue. The lack of a pectoral muscle elimination step in CADs is known to degrade the performance of detection algorithms, while digitization artifacts in the background may introduce noise in the training procedure. Therefore pectoral muscle and background segmentation are important preprocessing steps for inference over these biomedical images. There is a large variation in shape, size and density of pectoral muscles in mammograms and previously to this work only shallow methods based on simple statistics and image processing filters were analysed in this tasks, yielding suboptimal and non-generalizable results. As far as the authors know, this is the first work containing experimental evaluations of Deep Learning approaches for these tasks. We show that Deep Neural Networks comprise the state-of-the-art of current breast region segmentation algorithms, achieving significantly higher results than the baselines in several objective metrics for almost all datasets and tasks evaluated in this study.

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If you use any of the models or code in this website, please be sure to cite both this research's paper [1] and the basis implementation for the segmentation Deep Neural Networks [2]:
[1] "Exploring Deep-Based Approaches for Semantic Segmentation of Mammographic Images". Oliveira, Hugo; Avelar, Claudio; Machado, Alexei; Araujo, Arnaldo; and dos Santos Jefersson. The 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018).


If you have any doubt regarding the paper, methodology or code, please contact oliveirahugo@dcc.ufmg.br and jefersson@dcc.ufmg.br.