| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2023-11-22 |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| タイトル |
|
|
タイトル |
Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Miyake, Noriaki
Lu, Huinmin
神谷, 亨
Aoki, Takatoshi
Kido, Shoji
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The temporal subtraction technique is a useful tool for computer aided diagnosis (CAD) in visual screening. The technique subtracts the previous image set from the current one for the same subject to emphasize temporal changes and/or new abnormalities. However, it is difficult to obtain a clear subtraction image without subtraction image artifacts. VoxelMorph in deep learning is a useful method, as preparing large training datasets is difficult for medical image analysis, and the possibilities of incorrect learning, gradient loss, and overlearning are concerns. To overcome this problem, we propose a new method for generating temporal subtraction images of thoracic multi-detector row computed tomography (MDCT) images based on ResidualVoxelMorph, which introduces a residual block to VoxelMorph to enable flexible positioning at a low computational cost. Its high learning efficiency can be expected even with a limited training set by introducing residual blocks to VoxelMorph. We performed our method on 84 clinical images and evaluated based on three-fold cross-validation. The results showed that the proposed method reduced subtraction image artifacts on root mean square error (RMSE) by 11.3% (p < 0.01), and its effectiveness was verified. That is, the proposed temporal subtraction method for thoracic MDCT improves the observer’s performance. |
|
言語 |
en |
| 書誌情報 |
en : Applied Sciences
巻 12,
号 17,
p. 8542,
発行日 2022-08-26
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| 出版社 |
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|
出版者 |
MDPI |
| DOI |
|
|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/app12178542 |
| 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 |
2076-3417 |
| 著作権関連情報 |
|
|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CCBY) license. |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
digital healthcare |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
medical imaging analytics |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
computer aided diagnosis |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
temporal subtraction technique |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
nonrigid image registration |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
deep neural network |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
VoxelMorph |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
residual blocks |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
|
|
値 |
yes |
LaSEINE-2022_016.pdf (1.6 MB)
