Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images

Sahar Zafari, Mazen Diab, Tuomas Eerola, Summer E. Hanson, Gregory P. Reece, Gary J. Whitman, Mia K. Markey, Krishnaswamy Ravi-Chandar, Alan Bovik, Heikki Kälviäinen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Richard Boyle, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Xu
Number of pages12
ISBN (Print)9783030337193
StatePublished - 2019
Event14th International Symposium on Visual Computing, ISVC 2019 - Nevada, United States
Duration: Oct 7 2019Oct 9 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11844 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Symposium on Visual Computing, ISVC 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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