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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 10/2019

06-03-2019 | Original Article

A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts

Authors: Tatyana Ivanovska, Thomas G. Jentschke, Amro Daboul, Katrin Hegenscheid, Henry Völzke, Florentin Wörgötter

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2019

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Abstract

Purpose

The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

Methods

We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed.

Results

The average Dice coefficient for the breast parenchyma is \(92.5\%\pm 0.011\), which outperforms the classical state-of-the-art approach by a margin of \(9\%\).

Conclusion

The proposed solution is accurate and highly efficient and has potential to be applied for big epidemiological data with thousands of participants.
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Metadata
Title
A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts
Authors
Tatyana Ivanovska
Thomas G. Jentschke
Amro Daboul
Katrin Hegenscheid
Henry Völzke
Florentin Wörgötter
Publication date
06-03-2019
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 10/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-01928-y

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