| Presentation | 2019-01-22 Super-resolution of μCT image about dissected lung tissue using Adversarial Dense U-net Tong Zheng, Hirohisa Oda, Holger R. Roth, Masahiro Oda, Shota Nakamura, Kensaku Mori, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | μCT images capture three dimensional structures of tissues with a very high resolution of 100 micrometer or smaller. fine structures such as invasion of lung cancer. However, μCT cannot be used for clinical diagnosis. Although the clinical CT images are used for preoperative diagnosis, it cannot obtain detailed information of tumors. We are planning super-resolution of clinical CT images to μCT-level. As an initial trial, we perform super-resolution of downsampled μCT images to original resolution level. In this paper, we propose Adversarial Dense U-net, based on Dense U-net and Generative Adversarial Network. The network structure including U-net with dense blocks, while a discriminator was used to distinguish whether the output image is a super-resoluted image or an original image. Experiments were performed on μCT images of five cases of dissected lung tissues, and another one case was used for testing. We performed experiments on both 2D and 3D network structure. We evaluated results both quantitatively and qualitatively. Experimental results demonstrated that our proposed method has a SSIM (Structural Similarity) of 0.967 (3D) and 0.946 (2D). |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | μCT / Dense U-net / Generative Adversarial Network / super-resolution |
| Paper # | MI2018-61 |
| Date of Issue | 2019-01-15 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2019/1/22(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Chair | Kensaku Mori(Nagoya Univ.) |
| Vice Chair | Yoshiki Kawata(Tokushima Univ.) / Yuichi Kimura(Kinki Univ.) |
| Secretary | Yoshiki Kawata(Aichi Inst. of Tech.) / Yuichi Kimura(Nagoya Inst. of Tech.) |
| Assistant | Ryo Haraguchi(Univ. of Hyogo) / Yasushi Hirano(Yamaguchi Univ.) |
| Paper Information | |
| Registration To | Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Super-resolution of μCT image about dissected lung tissue using Adversarial Dense U-net |
| Sub Title (in English) | |
| Keyword(1) | μCT |
| Keyword(2) | Dense U-net |
| Keyword(3) | Generative Adversarial Network |
| Keyword(4) | super-resolution |
| 1st Author's Name | Tong Zheng |
| 1st Author's Affiliation | Graduate School of Informatics, Nagoya University(Nagoya University) |
| 2nd Author's Name | Hirohisa Oda |
| 2nd Author's Affiliation | Graduate School of Information Science(Nagoya University) |
| 3rd Author's Name | Holger R. Roth |
| 3rd Author's Affiliation | Graduate School of Informatics, Nagoya University(Nagoya University) |
| 4th Author's Name | Masahiro Oda |
| 4th Author's Affiliation | Graduate School of Informatics, Nagoya University(Nagoya University) |
| 5th Author's Name | Shota Nakamura |
| 5th Author's Affiliation | Nagoya University Graduate School of Medicine(Nagoya University) |
| 6th Author's Name | Kensaku Mori |
| 6th Author's Affiliation | Graduate School of Informatics, Nagoya University/Research Center for Medical Bigdata, National Institute of Informatics(Nagoya University/NII) |
| Date | 2019-01-22 |
| Paper # | MI2018-61 |
| Volume (vol) | vol.118 |
| Number (no) | MI-412 |
| Page | pp.pp.7-12(MI), |
| #Pages | 6 |
| Date of Issue | 2019-01-15 (MI) |