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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Binary polyp-size classification based on deep-learned spatial information

http://hdl.handle.net/2237/0002001718
http://hdl.handle.net/2237/0002001718
b6fc0ba8-39b4-4472-9803-9d8a8a7c7e61
名前 / ファイル ライセンス アクション
IJCARS2021_SizeEst_hitoh_NUrepository.pdf IJCARS2021_SizeEst_hitoh_NUrepository.pdf (2.1 MB)
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

× Itoh, Hayato

en Itoh, Hayato

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Oda, Masahiro

× Oda, Masahiro

en Oda, Masahiro

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Jiang, Kai

× Jiang, Kai

en Jiang, Kai

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Mori, Yuichi

× Mori, Yuichi

en Mori, Yuichi

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Misawa, Masashi

× Misawa, Masashi

en Misawa, Masashi

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Kudo, Shin-Ei

× Kudo, Shin-Ei

en Kudo, Shin-Ei

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Imai, Kenichiro

× Imai, Kenichiro

en Imai, Kenichiro

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Ito, Sayo

× Ito, Sayo

en Ito, Sayo

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Hotta, Kinichi

× Hotta, Kinichi

en Hotta, Kinichi

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Mori, Kensaku

× Mori, Kensaku

en Mori, Kensaku

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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 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
内容記述タイプ Abstract
出版者
言語 en
出版者 Springer
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
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
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