| Item type |
デフォルトアイテムタイプ（フル）その２(1) |
| 公開日 |
2025-12-15 |
| タイトル |
|
|
タイトル |
A Simulation Study Towards Sparse-View Spectral X-ray CT Segmentation Using Spectrum-Aware Deep Neural Networks |
|
言語 |
en |
| 作成者 |
WANG, Siqi
YATAGAWA, Tatsuya
OHTAKE, Yutaka
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
This is an Accepted Manuscript of an article published by Taylor & Francis in Nondestructive Testing and Evaluation on 24 Oct. 2024, available online: https://doi.org/10.1080/10589759.2024.2419914 |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
CT image segmentation |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
spectral X-ray CT |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Spectral X-ray computed tomography (SXCT) is able to provide richer spectral information than conventional X-ray CT, thereby improving the capability of discriminating materials in segmentation tasks. However, segmentation algorithms for conventional CT images often lose their performance in material segmentation with SXCT images due to the spectrumdependent attenuation properties of X-rays. Recently, deep neural networks (DNNs) have demonstrated their effectiveness in segmentation tasks. However, processing SXCT with DNNs requires significant memory resources and may take significant computation time. To address these issues, this paper introduces a simulation study towards realizing deeplearning- based SXCT image segmentation. The proposed method employs a convolutional neural network (CNN) that selectively utilizes the significant spectrum channels for accurate material segmentation. The proposed method builds upon a prior study on adaptive CT reconstruction, but to further enhance the performance, we introduce a self-attention-based spectrum-selection module. During training, the selection module assigns importance weights to spectrum channels based on the attention scores. Once the network has been trained, the proposed method can obtain satisfactory segmentation results from projection images at one or a combination of several spectrum channels recommended by the selection module. The experimental results demonstrate that the proposed method achieves a significant reduction in computation time while maintaining the segmentation quality. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Taylor & Francis |
|
言語 |
en |
| 日付 |
|
|
日付 |
2024-10-24 |
|
日付タイプ |
Issued |
| 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 |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1080/10589759.2024.2419914 |
| 収録物名 |
|
|
収録物名 |
Nondestructive Testing and Evaluation |
|
言語 |
en |
| 巻 |
|
|
巻 |
40 |
| 号 |
|
|
号 |
9 |
| 開始ページ |
|
|
開始ページ |
4259 |
| 終了ページ |
|
|
終了ページ |
4279 |
0102502201.pdf (2.8 MB)
