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Abstract #2420

Automatic Breast and Fibroglandular Tissue Segmentation Using Deep Learning by A Fully-Convolutional Residual Neural Network

Yang Zhang1, Vivian Youngjean Park2, Min Jung Kim2, Peter Chang3, Melissa Khy1, Daniel Chow1, Jeon-Hor Chen1, Alex Luk1, and Min-Ying Su1

1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea, 3Department of Radiology, University of California, San Francisco, CA, United States

A deep learning method using the fully-convolutional residual neural network (FCR-NN) was applied to segment the whole breast and fibroglandular tissue in 289 patients. The Dice similarity coefficient (DSC) value and accuracy were calculated as evaluation metrics. For breast segmentation, the mean DSC was 0.85 with an accuracy of 0.93; for fibroglandular tissue segmentation, the mean DSC was 0.67 with an accuracy of 0.75. The percent density calculated from ground truth and network segmentations were correlated, and showed a high coefficient of r=0.9. The initial results are promising, suggesting deep learning has a potential to provide an efficient and reliable breast density segmentation tool.

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