Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts

Mika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Kenta Sakaguchi; Tatsuo Konishi; Koji Yamada; Yoshiaki Ozaki; Kazunari Ishii; Yougbum Lee

doi:10.1117/12.2562180

22 May 2020 Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts

Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Kenta Sakaguchi, Tatsuo Konishi, Koji Yamada, Yoshiaki Ozaki, Kazunari Ishii, Yougbum Lee

Author Affiliations +

Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131V (2020) https://doi.org/10.1117/12.2562180
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium

Abstract

This study is aimed to automatically segment mammary gland region into scattered mammary glands and fatty breasts using deep learning method. Total 433 mediolateral oblique-view mammograms of Japanese women were collected and confirmed for scattered mammary glands or fatty breasts; using BI-RADS’s classification. First, manually contoured mammary gland regions were determined for all mammograms as ground truths by three certified radiological technologists. Second, the U-net model was employed to segment the mammary gland region automatically. This model is a type of convolutional neural network (CNN) mainly aimed at medical image segmentation. The segmentation accuracies were assessed based on five criteria, Dice coefficients, breast densities, mean gray values, centroids, and sizes of mammary gland region. The Dice coefficient was 0.915. The mean size of mammary gland regions obtained by the Unet was 8.7% larger than that of the ground truths. The mean centroid coordinates of mammary gland regions by the U-net were shifted 1.6 and 5.4 mm on average in mediolateral and craniocaudal directions, respectively from ground truths. The mean gray value of mammary gland regions obtained by the U-net was only 0.4% higher compared with ground truths. The resultant difference was 0.4% on average in breast densities between ground truths and the segmented mammary gland regions. We found significant similarity in the ground truths and the data generated by deep learning on all the parameters, thereby attesting the efficacy of this method for segmenting the mammary gland regions of not only the dense breasts but also the scattered mammary gland- and fatty- breasts.

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Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Kenta Sakaguchi, Tatsuo Konishi, Koji Yamada, Yoshiaki Ozaki, Kazunari Ishii, and Yougbum Lee "Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131V (22 May 2020); https://doi.org/10.1117/12.2562180

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KEYWORDS

Mammary gland

Breast

Image segmentation

Mammography

Tissues

Artificial intelligence

Convolutional neural networks

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