Rationale and Objectives
Computer-aided methods have been widely applied to diagnose lesions on breast magnetic
resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning
Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire
set of images and detect suspicious lesions.
Materials and Methods
Two DCE-MRI datasets were used, 241 patients acquired using non–fat-sat sequence for
training, and 98 patients acquired using fat-sat sequence for testing. All patients
have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means
clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with
ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented
tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance,
and the trade-off between sensitivity and specificity, was analyzed using free response
receiver operating characteristic.
Results
When the precontrast and subtraction image of both breasts were used as input, the
false positive from the heart and normal parenchymal enhancements could be minimized.
The training set had 1469 positive slices (containing lesion) and 9135 negative slices.
In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing
dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79.
When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the
training and 98 of 98 (100%) lesions in the testing datasets were identified.
Conclusion
Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize,
and segment lesions. This may be integrated with other artificial intelligence algorithms
to develop a fully automatic breast MRI diagnostic system.
Key Words
Abbreviations:
3D-FLASH (three-dimensional fast low angle shot), AFROC (Alternative Free-response ROC), AI (Artificial Intelligence), BI-RADS (Breast Imaging Reporting and Data System), CAD (Computer-Aided Diagnosis), CNN (Convolutional Neural Network), DCE (Dynamic Contrast-Enhanced), DSC (Dice Similarity Coefficient), FCM (Fuzzy C-Means), FPN (Feature Pyramid Network), FROC (Free-response ROC), IoU (Intersection over Union), MIP (Maximum Intensity Projection), TE (Echo Time), TR (Repetition Time), R-CNN (Regional Convolutional Neural Network), ROC (Receiver Operating Characteristic), ROI (Region Of Interest)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 11, 2020
Accepted:
December 3,
2020
Received in revised form:
December 2,
2020
Received:
November 11,
2020
Identification
Copyright
© 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
