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13 July 2022 Robustness of a U-net model for different image processing types in segmentation of the mammary gland region
Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Hiroto Kimura, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Seiun Nin, Kazunari Ishii, Yongbum Lee
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Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860T (2022) https://doi.org/10.1117/12.2624139
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium
Abstract
Many studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with variousimage processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1–4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1–4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types.
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Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Hiroto Kimura, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Seiun Nin, Kazunari Ishii, and Yongbum Lee "Robustness of a U-net model for different image processing types in segmentation of the mammary gland region", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860T (13 July 2022); https://doi.org/10.1117/12.2624139
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KEYWORDS
Image segmentation
Breast
Mammary gland
Image processing
Mammography
Image compression
Signal attenuation
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