27 February 2018 Deep convolutional neural network for mammographic density segmentation
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Abstract
Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists’ segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p < 0.0001) by two-tailed paired t-test. This study demonstrated that the DCNN approach has the potential to replace radiologists’ interactive thresholding in PD estimation on DMs.
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Jun Wei, Jun Wei, Songfeng Li, Songfeng Li, Heang-Ping Chan, Heang-Ping Chan, Mark A. Helvie, Mark A. Helvie, Marilyn A. Roubidoux, Marilyn A. Roubidoux, Yao Lu, Yao Lu, Chuan Zhou, Chuan Zhou, Lubomir Hadjiiski, Lubomir Hadjiiski, Ravi K. Samala, Ravi K. Samala, } "Deep convolutional neural network for mammographic density segmentation", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753I (27 February 2018); doi: 10.1117/12.2293351; https://doi.org/10.1117/12.2293351
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