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乳腺磁共振成像中的机器学习

Machine learning in breast MRI.

作者信息

Reig Beatriu, Heacock Laura, Geras Krzysztof J, Moy Linda

机构信息

The Department of Radiology, New York University School of Medicine, New York, New York, USA.

Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.

出版信息

J Magn Reson Imaging. 2020 Oct;52(4):998-1018. doi: 10.1002/jmri.26852. Epub 2019 Jul 5.

DOI:10.1002/jmri.26852
PMID:31276247
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7085409/
Abstract

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.

摘要

机器学习技术已在医学成像的数据提取和分析方面取得了显著进展。随着越来越精确的三维乳腺和病变分割技术的出现，机器学习在乳腺磁共振成像（MRI）中的应用继续迅速扩展，这使得放射科医生水平的解读（如BI-RADS词典）、来自先进多参数成像技术的数据以及诸如遗传风险标志物等患者层面的数据得以结合。乳腺MRI特征提取的进展已实现了快速的数据集分析，这在大型多机构汇总数据分析中具有前景。本综述的目的是概述用于乳腺MRI的机器学习和深度学习技术，包括监督和无监督方法、乳腺解剖分割和病变分割。最后，探讨机器学习的作用、当前局限性以及其在纹理分析、放射组学和放射基因组学方面的未来应用。证据水平：3 技术效能阶段：2 《磁共振成像杂志》2019年。《磁共振成像杂志》2020年；52:998 - 1018。

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本文引用的文献

[1]
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Eur J Radiol. 2019-2-27

[2]
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Diagn Interv Imaging. 2019-3-26

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J Magn Reson Imaging. 2019-3-29

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Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis.

J Magn Reson Imaging. 2019-3-27

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Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

Nature. 2019-3-13

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Tomography. 2019-3

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Clin Cancer Res. 2019-3-6

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Int J Comput Assist Radiol Surg. 2019-3-6

[10]
Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Acad Radiol. 2019-1-31

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