一种使用具有强烈伪影的磁共振数据进行高效分析乳房体积和纤维腺体组织的深度学习框架。
A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.
机构信息
Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany.
Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Fleischmannstr. 42-44, 17475, Greifswald, Germany.
出版信息
Int J Comput Assist Radiol Surg. 2019 Oct;14(10):1627-1633. doi: 10.1007/s11548-019-01928-y. Epub 2019 Mar 6.
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 [Formula: see text], which outperforms the classical state-of-the-art approach by a margin of [Formula: see text].
CONCLUSION
The proposed solution is accurate and highly efficient and has potential to be applied for big epidemiological data with thousands of participants.
目的
本工作的主要目的是开发、应用和评估一种在含有强烈伪影（包括强度不均匀性）的磁共振成像数据中进行乳房密度估计的有效方法。
方法
我们提出了一种用于乳房密度估计的流水线，它包括强度不均匀性校正、乳房体积分割、乳头提取和纤维腺体组织分割。对于分割步骤，我们使用了一个知名的深度学习架构。
结果
乳房实质的平均 Dice 系数为[Formula: see text]，优于经典的最先进方法[Formula: see text]。
结论
所提出的解决方案准确且高效，具有应用于包含数千名参与者的大型流行病学数据的潜力。
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