Abstract
Pectoral muscle segmentation is a crucial step in various computer-aided applications of breast Magnetic Resonance Imaging (MRI). Due to imaging artifact and homogeneity between the pectoral and breast regions, the pectoral muscle boundary estimation is not a trivial task. In this paper, a fully automatic segmentation method based on deep learning is proposed for accurate delineation of the pectoral muscle boundary in axial breast MR images. The proposed method involves two main steps: pectoral muscle segmentation and boundary estimation. For pectoral muscle segmentation, a model based on the U-Net architecture is used to segment the pectoral muscle from the input image. Next, the pectoral muscle boundary is estimated through candidate points detection and contour segmentation. The proposed method was evaluated quantitatively with two real-world datasets, our own private dataset, and a publicly available dataset. The first dataset includes 12 patients breast MR images and the second dataset consists of 80 patients breast MR images. The proposed method achieved a Dice score of 95% in the first dataset and 89% in the second dataset. The high segmentation performance of the proposed method when evaluated on large scale quantitative breast MR images confirms its potential applicability in future breast cancer clinical applications.
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Acknowledgments
This project was supported in part by the Academy of Finland (Cell vision project, Decision No. 313598); and The National Institutes of Health (R01CA143190 and R01CA203984). This study was approved by The University of Texas MD Anderson Cancer Center (protocol number 2015-1117). The authors would like to acknowledge the help received from Mary Catherine Bordes at The University of Texas MD Anderson Cancer Center for collecting the MRI datasets.
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Zafari, S. et al. (2019). Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_26
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DOI: https://doi.org/10.1007/978-3-030-33720-9_26
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