| Presentation | 2016-02-22 Deep Learning Based 3D Medical Volume Super Resolution Yuto Kondo, Qiaochu Zhao, Yutaro Iwamoto, Xian-Hua Han, Yan-Wei Chen, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Image Restoration / Super Resolution / Deep Learning |
| Paper # | ITS2015-61,IE2015-103 |
| Date of Issue | 2016-02-15 (ITS, IE) |
| Conference Information | |
| Committee | ITS / IE / ITE-AIT / ITE-HI / ITE-ME / ITE-MMS / ITE-CE |
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| Conference Date | 2016/2/22(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Tomotaka Nagaosa(Kanto Gakuin Univ.) / Seishi Takamura(NTT) / Tokiichiro Takahashi(TDU) / Masayuki Sato(Univ. of Kitakyushu) / Kazuhito Murakami(Aichi Prefectural Univ.) / Eiichi Miyashita(NHK) / Koji Minami(Mitsubishi Electric Corp.) |
| Vice Chair | Masahiro Fujii(Utsunomiya Univ.) / Tomotaka Wada(Kansai Univ.) / Takayuki Hamamoto(Tokyo Univ. of Science) / Atsuro Ichigaya(NHK) / / / Miki Haseyama(Hokkaido Univ.) |
| Secretary | Masahiro Fujii(AIST) / Tomotaka Wada(Utsunomiya Univ.) / Takayuki Hamamoto(NTT) / Atsuro Ichigaya(Chiba Inst. of Tech.) / (Tokyo Polytechnic Univ.) / (NICT) / Miki Haseyama(NTT) / (NHK) / (NHK) |
| Assistant | Kohei Ohno(Meiji Univ.) / Tetsuya Manabe(Saitama Univ.) / Keita Takahashi(Nagoya Univ.) / Kei Kawamura(KDDI R&D Labs.) |
| Paper Information | |
| Registration To | Technical Committee on Intelligent Transport Systems Technology / Technical Committee on Image Engineering / Technical Group on Artistic Image Technology / Technical Group on Human Information / Technical Group on Media Engineering / Technical Group on Multi-media Storage / Technical Group on Consumer Electronics |
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| Language | JPN |
| 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 |
| 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.) |
| Date | 2016-02-22 |
| Paper # | ITS2015-61,IE2015-103 |
| Volume (vol) | vol.115 |
| Number (no) | ITS-458,IE-459 |
| Page | pp.pp.29-34(ITS), pp.29-34(IE), |
| #Pages | 6 |
| Date of Issue | 2016-02-15 (ITS, IE) |