On Segmentation of Pectoral Muscle in Digital Mammograms by Means of Deep Learning

• Published in: IEEE access Vol. 8; pp. 204173 - 204182
• Main Authors: Soleimani, Hossein, Michailovich, Oleg V.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomy; Artificial neural networks; Breast; Breast cancer; CAI; Computer assisted instruction; Datasets; Deep learning; digital mammography; Image processing; Image segmentation; Machine learning; Mammography; Muscles; pectoral muscle; Prediction algorithms; Reliability; segmentation; Shortest-path problems; Spatial resolution; Task analysis; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3036662

Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography. Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle). Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confusable with some elements of breast anatomy due to their morphological and photometric similarity. As a result, the problem of automatic detection and segmentation of pectoral muscle in MLO mammograms remains a challenging task, innovative approaches to which are still required and constantly searched for. To address this problem, the present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction (deep learning) and graph-based image processing. In particular, the proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary at different levels of spatial resolution. Subsequently, the predictions are used by the second stage of the algorithm, in which the desired boundary is recovered as a solution to the shortest path problem on a specially designed graph. The proposed algorithm has been tested on three different datasets (i.e., MIAS, CBIS-DDSm, and InBreast) using a range of quantitative metrics. The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing. The average values of dice similarity coefficient (DSC) and accuracy (ACC) on the mentioned three datasets are 97.22 ± 1.96% and 99.64±.27%, respectively.