CLC number: TN29;TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-03-22
Cited: 0
Clicked: 7393
Citations: Bibtex RefMan EndNote GB/T7714
Hao YU, Changjiang ZHOU, Wei ZHANG, Liqiang WANG, Qing YANG, Bo YUAN. A three-dimensional measurement method for binocular endoscopes based on deep learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 653-660.
@article{title="A three-dimensional measurement method for binocular endoscopes based on deep learning",
author="Hao YU, Changjiang ZHOU, Wei ZHANG, Liqiang WANG, Qing YANG, Bo YUAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="4",
pages="653-660",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000679"
}
%0 Journal Article
%T A three-dimensional 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 Frontiers of Information Technology & Electronic Engineering
%V 23
%N 4
%P 653-660
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000679
TY - JOUR
T1 - A three-dimensional measurement method for binocular endoscopes based on deep learning
A1 - Hao YU
A1 - Changjiang ZHOU
A1 - Wei ZHANG
A1 - Liqiang WANG
A1 - Qing YANG
A1 - Bo YUAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 653
EP - 660
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000679
Abstract: 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 three-dimensional (3D) 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 is 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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