Uncertainty meets 3D-spatial feature in colonoscopic polyp-size determination
This paper presents a new 3D-spatial feature extraction from a 2D colonoscopic image for polyp-size estimation. The polyp-size estimation poses potential demands on colonoscopy, since an endoscopist's subjective estimation is apt to result in uncertain polyp-size determination. This uncertain d...
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| Vydáno v: | Computer methods in biomechanics and biomedical engineering. Ročník 10; číslo 3; s. 289 - 298 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Taylor & Francis
04.05.2022
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| ISSN: | 2168-1163, 2168-1171 |
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| Abstract | This paper presents a new 3D-spatial feature extraction from a 2D colonoscopic image for polyp-size estimation. The polyp-size estimation poses potential demands on colonoscopy, since an endoscopist's subjective estimation is apt to result in uncertain polyp-size determination. This uncertain determination is derived from the lack of 3D-spatial information. Even though a previous work clarified that depth estimation mitigates uncertainty in binary polyp-size classification, precise estimation of 3D structure from a colonoscopic image(s) remains an unsolved challenge in medical image analysis. This work proposes a 3D-spatial feature that expresses a polyp's precise 3D shape to mitigate uncertainty in polyp-size determination. First, we introduce an accurate depth estimation method to capture the 3D structure of a colon. Next, we integrate depth estimation and polyp localisation to extract a 3D polyp shape as a feature. Finally, we achieve polyp-size estimation by statistical learning of extracted features. The experimental results demonstrated the validity both of our depth estimation and 3D-spatial feature. Compared with deep RGB and RGB-D convolutional neural networks (CNNs), a shallow CNN with the proposed 3D-spatial feature achieved a more accurate polyp-size estimation with a mean absolute error of 1.36 mm, whereas the one of the deep CNN is 3.11 mm. |
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| AbstractList | This paper presents a new 3D-spatial feature extraction from a 2D colonoscopic image for polyp-size estimation. The polyp-size estimation poses potential demands on colonoscopy, since an endoscopist's subjective estimation is apt to result in uncertain polyp-size determination. This uncertain determination is derived from the lack of 3D-spatial information. Even though a previous work clarified that depth estimation mitigates uncertainty in binary polyp-size classification, precise estimation of 3D structure from a colonoscopic image(s) remains an unsolved challenge in medical image analysis. This work proposes a 3D-spatial feature that expresses a polyp's precise 3D shape to mitigate uncertainty in polyp-size determination. First, we introduce an accurate depth estimation method to capture the 3D structure of a colon. Next, we integrate depth estimation and polyp localisation to extract a 3D polyp shape as a feature. Finally, we achieve polyp-size estimation by statistical learning of extracted features. The experimental results demonstrated the validity both of our depth estimation and 3D-spatial feature. Compared with deep RGB and RGB-D convolutional neural networks (CNNs), a shallow CNN with the proposed 3D-spatial feature achieved a more accurate polyp-size estimation with a mean absolute error of 1.36 mm, whereas the one of the deep CNN is 3.11 mm. |
| Author | Itoh, Hayato Hotta, Kinichi Ito, Sayo Oda, Masahiro Misawa, Masashi Imai, Kenichiro Jiang, Kai Mori, Yuichi Kudo, Shin-Ei Mori, Kensaku |
| Author_xml | – sequence: 1 givenname: Hayato surname: Itoh fullname: Itoh, Hayato email: hitoh@mori.m.is.nagoya-u.ac.jp organization: Graduate School of Informatics, Nagoya University – sequence: 2 givenname: Masahiro surname: Oda fullname: Oda, Masahiro organization: Nagoya University – sequence: 3 givenname: Kai surname: Jiang fullname: Jiang, Kai organization: Graduate School of Informatics, Nagoya University – sequence: 4 givenname: Yuichi surname: Mori fullname: Mori, Yuichi organization: Showa University Northern Yokohama Hospital – sequence: 5 givenname: Masashi surname: Misawa fullname: Misawa, Masashi organization: Showa University Northern Yokohama Hospital – sequence: 6 givenname: Shin-Ei surname: Kudo fullname: Kudo, Shin-Ei organization: Showa University Northern Yokohama Hospital – sequence: 7 givenname: Kenichiro surname: Imai fullname: Imai, Kenichiro organization: Shizuoka Cancer Center – sequence: 8 givenname: Sayo surname: Ito fullname: Ito, Sayo organization: Shizuoka Cancer Center – sequence: 9 givenname: Kinichi surname: Hotta fullname: Hotta, Kinichi organization: Shizuoka Cancer Center – sequence: 10 givenname: Kensaku surname: Mori fullname: Mori, Kensaku organization: National Institute of Informatics |
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| SubjectTerms | computer-aided diagnosis depth estimation polyp localisation Polyp-size estimation |
| Title | Uncertainty meets 3D-spatial feature in colonoscopic polyp-size determination |
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