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| Presentation | 2019-01-23 14:00 Unsupervised Shadow Detection for Ultrasound Images by Deep Learning Suguru Yasutomi (FLL), Akira Sakai (FATEC), Masaaki Komatsu (Riken), Ryu Matsuoka, Reina Komatsu, Tatsuya Arakaki, Mayumi Tokunaka (Showa-U), Hidenori Machino, Kazuma Kobayashi (NCC), Ken Asada (Riken), Syuzo Kaneko (NCC), Akihiko Sekizawa (Showa-U), Ryuji Hamamoto (Riken)  MI2018-96 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Medical ultrasound is widely used for diagnosing internal organs since it is non-invasive. Shadows are often appear in ultrasound images, and they hinder diagnosing and image processing. Detecting shadows from the images is important problem in such cases. In this paper, we propose a method to detect shadows using deep learning which is learned by unsupervised way. Specifically, we construct an autoencoder that splits input images into shadows and other contents, and combines them to reconstruct the inputs. The autoencoder is learned to split by newly proposed losses that evaluates characteristics of ultrasound images and its shadows. Effectiveness of the proposed method is shown by experiments on images for embryonic heart diagnosis. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Ultrasound imaging / Shadow detection / Deep learning / Unsupervised learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 412, MI2018-96, pp. 151-156, Jan. 2019. | |
| Paper # | MI2018-96 | |
| Date of Issue | 2019-01-15 (MI) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | MI2018-96 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2019-01-22 - 2019-01-23 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2019-01-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Unsupervised Shadow Detection for Ultrasound Images by Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | Ultrasound imaging |
| Keyword(2) | Shadow detection |
| Keyword(3) | Deep learning |
| Keyword(4) | Unsupervised learning |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Suguru Yasutomi |
| 1st Author's Affiliation | Fujitsu Laboratories Ltd., Artificial Intelligence Laboratory (FLL) |
| 2nd Author's Name | Akira Sakai |
| 2nd Author's Affiliation | Fujitsu Advanced Technologies, Division of Development Platform Technology (FATEC) |
| 3rd Author's Name | Masaaki Komatsu |
| 3rd Author's Affiliation | RIKEN, AIP Center, Cancer Translational Research Team (Riken) |
| 4th Author's Name | Ryu Matsuoka |
| 4th Author's Affiliation | Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U) |
| 5th Author's Name | Reina Komatsu |
| 5th Author's Affiliation | Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U) |
| 6th Author's Name | Tatsuya Arakaki |
| 6th Author's Affiliation | Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U) |
| 7th Author's Name | Mayumi Tokunaka |
| 7th Author's Affiliation | Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U) |
| 8th Author's Name | Hidenori Machino |
| 8th Author's Affiliation | National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC) |
| 9th Author's Name | Kazuma Kobayashi |
| 9th Author's Affiliation | National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC) |
| 10th Author's Name | Ken Asada |
| 10th Author's Affiliation | RIKEN, AIP Center, Cancer Translational Research Team (Riken) |
| 11th Author's Name | Syuzo Kaneko |
| 11th Author's Affiliation | National Cancer Center Research Institute, Division of Molecular Modification and Cancer Biology (NCC) |
| 12th Author's Name | Akihiko Sekizawa |
| 12th Author's Affiliation | Showa University School of Medicine, Department of Obstetrics and Gynecology (Showa-U) |
| 13th Author's Name | Ryuji Hamamoto |
| 13th Author's Affiliation | RIKEN, AIP Center, Cancer Translational Research Team (Riken) |
| Speaker | Author-1 |
| Date Time | 2019-01-23 14:00:00 |
| Presentation Time | 60 minutes |
| Registration for | MI |
| Paper # | MI2018-96 |
| Volume (vol) | vol.118 |
| Number (no) | no.412 |
| Page | pp.151-156 |
| #Pages | 6 |
| Date of Issue | 2019-01-15 (MI) |
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