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Proceedings Paper

Deep convolutional neural network for mammographic density segmentation
Author(s): Jun Wei; Songfeng Li; Heang-Ping Chan; Mark A. Helvie; Marilyn A. Roubidoux; Yao Lu; Chuan Zhou; Lubomir Hadjiiski; Ravi K. Samala
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753I (27 February 2018); doi: 10.1117/12.2293351
Show Author Affiliations
Jun Wei, Univ. of Michigan Health System (United States)
Songfeng Li, Sun Yat-Sen Univ. (China)
Heang-Ping Chan, Univ. of Michigan Health System (United States)
Mark A. Helvie, Univ. of Michigan Health System (United States)
Marilyn A. Roubidoux, Univ. of Michigan Health System (United States)
Yao Lu, Sun Yat-Sen Univ. (China)
Chuan Zhou, Univ. of Michigan Health System (United States)
Lubomir Hadjiiski, Univ. of Michigan Health System (United States)
Ravi K. Samala, Univ. of Michigan Health System (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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