Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography

Ghesu FC, Wels M, Jerebko A, Sühling M, Hornegger J, Kelm BM (2014)

Publication Type: Conference contribution, Original article

Publication year: 2014

Publisher: Springer Verlag

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Series: Lecture Notes on Computer Science

Book Volume: 8331

Pages Range: 148-157

Conference Proceedings Title: Medical Computer Vision. Large Data in Medical Imaging

Event location: Nagoya, Japan JP

DOI: 10.1007/978-3-319-05530-5_15

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.

Authors with CRIS profile

Florin-Cristian Ghesu Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Joachim Hornegger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE) Siemens AG, Sector Corporate Technology DE Germany (DE)

How to cite

APA:

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

MLA:

Ghesu, Florin-Cristian, 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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