How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography

Mika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Takahiro Yamada; Mitsutaka Nemoto; Yuichi Kimura; Hisashi Handa; Hisashi Yoshida; Koji Abe; Masahiro Tada; Hitoshi Habe; Takashi Nagaoka; Yoshiaki Ozaki; Seiun Nin; Kazunari Ishii; Yongbum Lee

doi:10.1117/12.2581424

15 February 2021 How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography

Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Yoshiaki Ozaki, Seiun Nin, Kazunari Ishii, Yongbum Lee

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972V (2021) https://doi.org/10.1117/12.2581424
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

In individualized screening mammography, a breast density is important to predict potential risks of breast cancer incidence and missing lesions in mammographic diagnosis. Segmentation of the mammary gland region is required when focusing on missing lesions. A deep-learning method was recently developed to segment the mammary gland region. A large amount of ground truth (prepared by mammary experts) is required for highly accurate deep-learning practice; however, this work is time- and labor-intensive. To streamline the ground truth in deep learning, we investigated a difference in acquired mammary gland regions among multiple radiological technologists having various experience and reading levels, who shared the criteria on segmentation. If we can ignore a skill level for image reading, we can increase a number of training images. Three certified radiological technologists segmented the mammary gland region in 195 mammograms. The degree of coincidence among them was assessed with respect to seven factors which indicated the feature of segmented regions including the breast density and mean glandular dose, using Student’s t-test and Bland-Altman analysis. The assessments made by the three radiological technologists were consistent considering all factors, except the mean pixel value. Thus, we concluded that the ground truths prepared by multiple practitioners with different experiences can be accepted for the segmentation of the mammary gland region and they are applicable for training images if they stringently share the criteria on the segmentation.

Conference Presentation

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Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Yoshiaki Ozaki, Seiun Nin, Kazunari Ishii, and Yongbum Lee "How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972V (15 February 2021); https://doi.org/10.1117/12.2581424

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