A4 Conference proceedings

Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images

Open Access publication

Publication Details
Authors: Zafari Sahar, Diab Mazen, Eerola Tuomas, Hanson Summer E., Reece Gregory P., Whitman Gary J., Markey Mia K., Ravi-Chandar Krishnaswamy, Bovik Alan, Kälviäinen Heikki
Publisher: Springer Verlag (Germany): Series
Publication year: 2019
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: Proceedings of International Symposium on Visual Computing (ISVC 2019)
Journal acronym: LNCS
Volume number: 11844
Start page: 345
End page: 356
Number of pages: 12
ISBN: 978-3-030-33719-3
eISBN: 978-3-030-33720-9
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019103135999


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.

Last updated on 2020-20-03 at 10:03