Machine learning in breast MRI

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 interpretat...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2020-10, Vol.52 (4), p.998-1018
Main Authors: Reig, Beatriu, Heacock, Laura, Geras, Krzysztof J., Moy, Linda
Format: Article
Language:English
Subjects:
artificial intelligence
Breast
Breast - diagnostic imaging
Data analysis
deep learning
Feature extraction
Humans
Image segmentation
Imaging techniques
Learning algorithms
Lesions
Machine Learning
Magnetic Resonance Imaging
Medical imaging
Radiography
Radiomics
Retrospective Studies
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Summary: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.
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.26852