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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000679


A 3D measurement method for binocular endoscopes based on deep learning


Author(s):  Hao YU, Changjiang ZHOU, Wei ZHANG, Liqiang WANG, Qing YANG, Bo YUAN

Affiliation(s):  State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yuanbo@zju.edu.cn

Key Words:  Binocular endoscope, Three-dimensional measurement, Deep learning, Disparity estimation


Hao YU, Changjiang ZHOU, Wei ZHANG, Liqiang WANG, Qing YANG, Bo YUAN. A 3D measurement method for binocular endoscopes based on deep learning[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Hao YU, Changjiang ZHOU, Wei ZHANG, Liqiang WANG, Qing YANG, Bo YUAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000679"
}

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%T A 3D measurement method for binocular endoscopes based on deep learning
%A Hao YU
%A Changjiang ZHOU
%A Wei ZHANG
%A Liqiang WANG
%A Qing YANG
%A Bo YUAN
%J Journal of Zhejiang University SCIENCE C
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%D 1998
%I Zhejiang University Press & Springer
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T1 - A 3D measurement method for binocular endoscopes based on deep learning
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A1 - Bo YUAN
J0 - Journal of Zhejiang University Science C
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DOI - 10.1631/FITEE.2000679


Abstract: 
In the practice of clinical endoscopy, the precise estimation of the lesion size is quite significant for diagnosis. In this paper, we propose a 3D (three-dimensional) measurement method for binocular endoscopes based on deep learning, which can overcome the poor robustness of the traditional binocular matching algorithm in texture-less areas. A simulated binocular image dataset is created from the target 3D data obtained by a 3D scanner and the binocular camera simulated by 3D rendering software to train a disparity estimation model for 3D measurement. The experimental results demonstrate that, compared with the traditional binocular matching algorithm, the proposed method improves the accuracy and disparity map generation speed by 48.9% and 90.5%, respectively. This can provide more accurate and reliable lesion size and improve the efficiency of endoscopic diagnosis.

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