5 February 2018 Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks
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Abstract
Deep learning methods have been shown to improve breast cancer diagnostic and prognostic decisions based on selected slices of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, incorporation of volumetric and temporal components into DCE-MRIs has not been well studied. We propose maximum intensity projection (MIP) images of subtraction MRI as a way to simultaneously include four-dimensional (4-D) images into lesion classification using convolutional neural networks (CNN). The study was performed on a dataset of 690 cases. Regions of interest were selected around each lesion on three MRI presentations: (i) the MIP image generated on the second postcontrast subtraction MRI, (ii) the central slice of the second postcontrast MRI, and (iii) the central slice of the second postcontrast subtraction MRI. CNN features were extracted from the ROIs using pretrained VGGNet. The features were utilized in the training of three support vector machine classifiers to characterize lesions as malignant or benign. Classifier performances were evaluated with fivefold cross-validation and compared based on area under the ROC curve (AUC). The approach using MIPs [AUC=0.88(se=0.01)] outperformed that using central-slices of either second postcontrast MRIs [0.80(se=0.02)] or second postcontrast subtraction MRIs [AUC=0.84(se=0.02)], at statistically significant levels.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Natalia O. Antropova, Natalia O. Antropova, Hiroyuki Abe, Hiroyuki Abe, Maryellen L. Giger, Maryellen L. Giger, } "Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks," Journal of Medical Imaging 5(1), 014503 (5 February 2018). https://doi.org/10.1117/1.JMI.5.1.014503 . Submission: Received: 10 October 2017; Accepted: 11 January 2018
Received: 10 October 2017; Accepted: 11 January 2018; Published: 5 February 2018
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