| Item type |
itemtype_ver1(1) |
| 公開日 |
2021-12-13 |
| タイトル |
|
|
タイトル |
Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task |
|
言語 |
en |
| 著者 |
Itoh, Hayato
Oda, Masahiro
Mori, Yuichi
Misawa, Masashi
Kudo, Shin-Ei
Imai, Kenichiro
Ito, Sayo
Hotta, Kinichi
Takabatake, Hirotsugu
Mori, Masaki
Natori, Hiroshi
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/s11548-021-02398-x |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Purpose: A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-aided diagnosis (CAD) system for colonoscopy. However, a straightforward application of existing depth-estimation methods to colonoscopic images is impossible or inappropriate due to several limitations of colonoscopes. In particular, the absence of ground-truth depth for colonoscopic images hinders the application of supervised machine learning methods. To circumvent these difficulties, we developed an unsupervised and accurate depth-estimation method. Method: We propose a novel unsupervised depth-estimation method by introducing a Lambertian-reflection model as an auxiliary task to domain translation between real and virtual colonoscopic images. This auxiliary task contributes to accurate depth estimation by maintaining the Lambertian-reflection assumption. In our experiments, we qualitatively evaluate the proposed method by comparing it with state-of-the-art unsupervised methods. Furthermore, we present two quantitative evaluations of the proposed method using a measuring device, as well as a new 3D reconstruction technique and measured polyp sizes. Results: Our proposed method achieved accurate depth estimation with an average estimation error of less than 1 mm for regions close to the colonoscope in both of two types of quantitative evaluations. Qualitative evaluation showed that the introduced auxiliary task reduces the effects of specular reflections and colon wall textures on depth estimation and our proposed method achieved smooth depth estimation without noise, thus validating the proposed method. Conclusions: We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information. |
|
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 |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1007/s11548-021-02398-x |
| 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 6,
p. 989-1001,
発行日 2021-06
|
| ファイル公開日 |
|
|
日付 |
2022-06-01 |
|
日付タイプ |
Available |
IJCARS2021_Depth_hitoh_NUrepository.pdf (4.6 MB)
