| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-10-03 |
| タイトル |
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タイトル |
Decomposition of musculoskeletal structures from radiographs using an improved CycleGAN framework |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Nakanishi, Naoki
大竹, 義人
Hiasa, Yuta
Gu, Yi
Uemura, Keisuke
Takao, Masaki
Sugano, Nobuhiko
佐藤, 嘉伸
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This paper presents methods of decomposition of musculoskeletal structures from radiographs into multiple individual muscle and bone structures. While existing solutions require dual-energy scan for the training dataset and are mainly applied to structures with high-intensity contrast, such as bones, we focused on multiple superimposed muscles with subtle contrast in addition to bones. The decomposition problem is formulated as an image translation problem between (1) a real X-ray image and (2) multiple digitally reconstructed radiographs, each of which contains a single muscle or bone structure, and solved using unpaired training based on the CycleGAN framework. The training dataset was created via automatic computed tomography (CT) segmentation of muscle/bone regions and virtually projecting them with geometric parameters similar to the real X-ray images. Two additional features were incorporated into the CycleGAN framework to achieve a high-resolution and accurate decomposition: hierarchical learning and reconstruction loss with the gradient correlation similarity metric. Furthermore, we introduced a new diagnostic metric for muscle asymmetry directly measured from a plain X-ray image to validate the proposed method. Our simulation and real-image experiments using real X-ray and CT images of 475 patients with hip diseases suggested that each additional feature significantly enhanced the decomposition accuracy. The experiments also evaluated the accuracy of muscle volume ratio measurement, which suggested a potential application to muscle asymmetry assessment from an X-ray image for diagnostic and therapeutic assistance. The improved CycleGAN framework can be applied for investigating the decomposition of musculoskeletal structures from single radiographs. |
| 書誌情報 |
en : Scientific Reports
巻 13,
号 1,
発行日 2023-05-25
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| 出版者 |
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出版者 |
Nature Research |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2045-2322 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1038/s41598-023-35075-x |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
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権利情報 |
$00A9 The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| 著者版フラグ |
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出版タイプ |
NA |
