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| Presentation | 2016-02-22 10:45 Deep Learning Based 3D Medical Volume Super Resolution Yuto Kondo, Qiaochu Zhao, Yutaro Iwamoto, Xian-Hua Han, Yan-Wei Chen (Ritsumeikan Univ.)  ITS2015-61 IE2015-103 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In medical diagnosis, high resolution images are necessary. However, it often takes too much processing time for MRI(Magnetic Resonance Imaging) or CT(Computed Tomography) to obtain the high resolution images, which leads to a burden of capturing images for a patient. To solve this problem, super resolution is proposed to generate higher resolution images from lower ones arithmetically. On the other hand, deep learning, as an effective machine learning method has many applications in variety fields. In this research, we applied the deep learning technique for medical image super resolution. The validity of our method is verified in 2D medical images. In addition, our method is also applicable to 3D images and the super resolution images are used for reconstruction of coronal plane and sagittal plane. Experiments showed that the use of our proposed method achieves superior results tested on an axial plane database. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image Restoration / Super Resolution / Deep Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 459, IE2015-103, pp. 29-34, Feb. 2016. | |
| Paper # | IE2015-103 | |
| Date of Issue | 2016-02-15 (ITS, IE) | |
| ISSN | Print edition: ISSN 0913-5685 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 | ITS2015-61 IE2015-103 |
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| Conference Information | |
| Committee | ITS IE ITE-AIT ITE-HI ITE-ME ITE-MMS ITE-CE |
| Conference Date | 2016-02-22 - 2016-02-23 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2016-02-ITS-IE-AIT-HI-ME-MMS-CE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep Learning Based 3D Medical Volume Super Resolution |
| Sub Title (in English) | |
| Keyword(1) | Image Restoration |
| Keyword(2) | Super Resolution |
| Keyword(3) | Deep Learning |
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| 1st Author's Name | Yuto Kondo |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Qiaochu Zhao |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Yutaro Iwamoto |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 4th Author's Name | Xian-Hua Han |
| 4th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 5th Author's Name | Yan-Wei Chen |
| 5th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2016-02-22 10:45:00 |
| Presentation Time | 15 minutes |
| Registration for | IE |
| Paper # | ITS2015-61, IE2015-103 |
| Volume (vol) | vol.115 |
| Number (no) | no.458(ITS), no.459(IE) |
| Page | pp.29-34 |
| #Pages | 6 |
| Date of Issue | 2016-02-15 (ITS, IE) |
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