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DCフィールド | 値 | 言語 |
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dc.contributor.author | Tong, Fei | en |
dc.contributor.author | Nakao, Megumi | en |
dc.contributor.author | Wu, Shuqiong | en |
dc.contributor.author | Nakamura, Mitsuhiro | en |
dc.contributor.author | Matsuda, Tetsuya | en |
dc.contributor.alternative | 中尾, 恵 | ja |
dc.contributor.alternative | 中村, 光宏 | ja |
dc.contributor.alternative | 松田, 哲也 | ja |
dc.date.accessioned | 2021-10-11T02:26:02Z | - |
dc.date.available | 2021-10-11T02:26:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 9781728119908 | - |
dc.identifier.uri | http://hdl.handle.net/2433/265386 | - |
dc.description | [2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 20-24 July 2020, Montreal, QC, Canada] | en |
dc.description.abstract | Computed tomography (CT) and magnetic resonance imaging (MRI) scanners measure three-dimensional (3D) images of patients. However, only low-dimensional local two-dimensional (2D) images may be obtained during surgery or radiotherapy. Although computer vision techniques have shown that 3D shapes can be estimated from multiple 2D images, shape reconstruction from a single 2D image such as an endoscopic image or an X-ray image remains a challenge. In this study, we propose X-ray2Shape, which permits a deep learning-based 3D organ mesh to be reconstructed from a single 2D projection image. The method learns the mesh deformation from a mean template and deep features computed from the individual projection images. Experiments with organ meshes and digitally reconstructed radiograph (DRR) images of abdominal regions were performed to confirm the estimation performance of the methods. | en |
dc.language.iso | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
dc.rights | This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 | en |
dc.subject | Shape | en |
dc.subject | Three-dimensional displays | en |
dc.subject | Liver | en |
dc.subject | Feature extraction | en |
dc.subject | X-ray imaging | en |
dc.subject | Two dimensional displays | en |
dc.subject | Image reconstruction | en |
dc.title | X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image | en |
dc.type | conference paper | - |
dc.type.niitype | Conference Paper | - |
dc.identifier.jtitle | 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | en |
dc.identifier.spage | 1608 | - |
dc.identifier.epage | 1611 | - |
dc.relation.doi | 10.1109/EMBC44109.2020.9176655 | - |
dc.textversion | author | - |
dc.identifier.artnum | 9176655 | - |
dc.identifier.pmid | 33018302 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 18H02766 | - |
datacite.awardNumber | 18K19918 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18H02766/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18K19918/ | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 統計呼吸動体モデルを軸とした寡分割高精度放射線治療技術の開発 | ja |
jpcoar.awardTitle | 圧縮センシングを応用した治療時生体臓器の高次状態復元 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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