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Open AccessArticle

Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows

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Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-Ku, Kawasaki, Kanagawa 211-8588, Japan
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RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan
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Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan
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Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
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Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
*
Authors to whom correspondence should be addressed.
Academic Editors: Byung-Gyu Kim and Jordi Solé-Casals
Appl. Sci. 2021, 11(3), 1127; https://doi.org/10.3390/app11031127
Received: 19 December 2020 / Revised: 19 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Machine Learning Methods with Noisy, Incomplete or Small Datasets)
Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments. View Full-Text
Keywords: ultrasound images; shadow detection; shadow estimation; deep learning; auto-encoders; semi-supervised learning ultrasound images; shadow detection; shadow estimation; deep learning; auto-encoders; semi-supervised learning
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MDPI and ACS Style

Yasutomi, S.; Arakaki, T.; Matsuoka, R.; Sakai, A.; Komatsu, R.; Shozu, K.; Dozen, A.; Machino, H.; Asada, K.; Kaneko, S.; Sekizawa, A.; Hamamoto, R.; Komatsu, M. Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows. Appl. Sci. 2021, 11, 1127. https://doi.org/10.3390/app11031127

AMA Style

Yasutomi S, Arakaki T, Matsuoka R, Sakai A, Komatsu R, Shozu K, Dozen A, Machino H, Asada K, Kaneko S, Sekizawa A, Hamamoto R, Komatsu M. Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows. Applied Sciences. 2021; 11(3):1127. https://doi.org/10.3390/app11031127

Chicago/Turabian Style

Yasutomi, Suguru; Arakaki, Tatsuya; Matsuoka, Ryu; Sakai, Akira; Komatsu, Reina; Shozu, Kanto; Dozen, Ai; Machino, Hidenori; Asada, Ken; Kaneko, Syuzo; Sekizawa, Akihiko; Hamamoto, Ryuji; Komatsu, Masaaki. 2021. "Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows" Appl. Sci. 11, no. 3: 1127. https://doi.org/10.3390/app11031127

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