Published in:
01-10-2019 | Magnetic Resonance Imaging | 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.