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Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
https://repo.qst.go.jp/records/49153
https://repo.qst.go.jp/records/49153b45be038-26ce-4c41-a889-a1cd63531406
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2018-08-29 | |||||
| タイトル | ||||||
| タイトル | Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Umehara, Kensuke
× Umehara, Kensuke× Ota, Junko× Ishida, Takayuki× Umehara, Kensuke× Ota, Junko× Ishida, Takayuki |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images. | |||||
| 書誌情報 |
Journal of digital imaging 巻 31, 号 4, p. 441-450, 発行日 2017-10 |
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| 出版者 | ||||||
| 出版者 | Springer | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0897-1889 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 29047035 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1007/s10278-017-0033-z | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://link.springer.com/article/10.1007/s10278-017-0033-z | |||||
