Conference contribution
(Original article)


Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography


Publication Details
Author(s): Ghesu F, Wels M, Jerebko A, Sühling M, Hornegger J, Kelm B
Title edited volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Publication year: 2014
Title of series: Lecture Notes on Computer Science
Volume: 8331
Conference Proceedings Title: Medical Computer Vision. Large Data in Medical Imaging
Pages range: 148-157
ISSN: 1611-3349

Event details
Event: Third International MICCAI Workshop, MCV 2013
Event location: Nagoya, Japan
Start date of the event: 26/09/2013
End date of the event: 26/09/2013

Abstract

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.



How to cite
APA: Ghesu, F., Wels, M., Jerebko, A., Sühling, M., Hornegger, J., & Kelm, B. (2014). Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography. In Medical Computer Vision. Large Data in Medical Imaging (pp. 148-157). Springer Verlag.

MLA: Ghesu, Florin C., et al. "Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography." Proceedings of the Third International MICCAI Workshop, MCV 2013, Nagoya, Japan Springer Verlag, 2014. 148-157.

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