| Item type |
itemtype_ver1(1) |
| 公開日 |
2021-12-13 |
| タイトル |
|
|
タイトル |
Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics |
|
言語 |
en |
| 著者 |
Itoh, Hayato
Nimura, Yukitaka
Mori, Yuichi
Misawa, Masashi
Kudo, Shin-Ei
Hotta, Kinichi
Ohtsuka, Kazuo
Saito, Shoichi
Saito, Yutaka
Ikematsu, Hiroaki
Hayashi, Yuichiro
Oda, Masahiro
Mori, Kensaku
|
| 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 |
|
|
言語 |
en |
|
権利情報 |
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11548-020-02255-3 |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Endocytoscopy |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
CAD |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Pathological pattern classification |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Machine learning |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Texture analysis |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Purpose: An endocytoscope is a new type of endoscope that enables users to perform conventional endoscopic observation and ultramagnified observation at the cell level. Although endocytoscopy is expected to improve the cost-effectiveness of colonoscopy, endocytoscopic image diagnosis requires much knowledge and high-level experience for physicians. To circumvent this difficulty, we developed a robust endocytoscopic (EC) image classification method for the construction of a computer-aided diagnosis (CAD) system, since real-time CAD can resolve accuracy issues and reduce interobserver variability. Method: We propose a novel feature extraction method by introducing higher-order symmetric tensor analysis to the computation of multi-scale topological statistics on an image, and we integrate this feature extraction with EC image classification. We experimentally evaluate the classification accuracy of our proposed method by comparing it with three deep learning methods. We conducted this comparison by using our large-scale multi-hospital dataset of about 55,000 images of over 3800 patients. Results: Our proposed method achieved an average 90% classification accuracy for all the images in four hospitals even though the best deep learning method achieved 95% classification accuracy for images in only one hospital. In the case with a rejection option, the proposed method achieved expert-level accurate classification. These results demonstrate the robustness of our proposed method against pit pattern variations, including differences of colours, contrasts, shapes, and hospitals. Conclusions: We developed a robust EC image classification method with novel feature extraction. This method is useful for the construction of a practical CAD system, since it has sufficient generalisation ability. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Springer |
|
言語 |
en |
| 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/10.1007/s11548-020-02255-3 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1861-6410 |
| 書誌情報 |
en : International Journal of Computer Assisted Radiology and Surgery
巻 15,
号 12,
p. 2049-2059,
発行日 2020-12
|
| ファイル公開日 |
|
|
日付 |
2021-12-13 |
|
日付タイプ |
Available |
IJCARS2020_EC_hitoh_NUrepository.pdf (3.3 MB)
