| Item type |
itemtype_ver1(1) |
| 公開日 |
2021-12-13 |
| タイトル |
|
|
タイトル |
Binary polyp-size classification based on deep-learned spatial information |
|
言語 |
en |
| 著者 |
Itoh, Hayato
Oda, Masahiro
Jiang, Kai
Mori, Yuichi
Misawa, Masashi
Kudo, Shin-Ei
Imai, Kenichiro
Ito, Sayo
Hotta, Kinichi
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/s11548-021-02477-z |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Colonoscopy |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Polyp-size classification |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Depth estimation |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Polyp localisation |
| 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 |
Deep learning |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Purpose: The size information of detected polyps is an essential factor for diagnosis in colon cancer screening. For example, adenomas and sessile serrated polyps that are ≥10 mm are considered advanced, and shorter surveillance intervals are recommended for smaller polyps. However, sometimes the subjective estimations of endoscopists are incorrect and overestimate the sizes. To circumvent these difficulties, we developed a method for automatic binary polyp-size classification between two polyp sizes: from 1 to 9 mm and ≥10 mm. Method: We introduce a binary polyp-size classification method that estimates a polyp’s three-dimensional spatial information. This estimation is comprised of polyp localisation and depth estimation. The combination of location and depth information expresses a polyp’s three-dimensional shape. In experiments, we quantitatively and qualitatively evaluate the proposed method using 787 polyps of both protruded and flat types. Results: The proposed method’s best classification accuracy outperformed the fine-tuned state-of-the-art image classification methods. Post-processing of sequential voting increased the classification accuracy and achieved classification accuracy of 0.81 and 0.88 for polyps ranging from 1 to 9 mm and others that are ≥10 mm. Qualitative analysis revealed the importance of polyp localisation even in polyp-size classification. Conclusions: We developed a binary polyp-size classification method by utilising the estimated three-dimensional shape of a polyp. Experiments demonstrated accurate classification for both protruded- and flat-type polyps, even though the flat type have ambiguous boundary between a polyp and colon wall. |
|
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/s11548-021-02477-z |
| 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 16,
号 10,
p. 1817-1828,
発行日 2021-10
|
| ファイル公開日 |
|
|
日付 |
2022-11-01 |
|
日付タイプ |
Available |
IJCARS2021_SizeEst_hitoh_NUrepository.pdf (2.1 MB)
