Full Text:   <2196>

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CLC number: TN29;TP391.4

On-line Access: 2022-04-20

Received: 2020-12-07

Revision Accepted: 2022-05-04

Crosschecked: 2021-03-22

Cited: 0

Clicked: 4736

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hao YU

https://orcid.org/0000-0001-9984-5051

Bo YUAN

https://orcid.org/0000-0002-3185-2690

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.653-660

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


A three-dimensional 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:  Key words: Binocular endoscope, Three-dimensional measurement, Deep learning, Disparity estimation


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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.

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doi="10.1631/FITEE.2000679"
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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.

基于深度学习的双目内窥镜三维测量方法

余浩1,周长江2,张伟1,王立强1,2,杨青1,2,袁波1
1浙江大学光电科学与工程学院现代光学仪器国家重点实验室,中国杭州市,310027
2之江实验室超级感知中心,中国杭州市,311100
摘要:在内窥镜临床检查中,病灶尺寸精确估计对诊断具有非常重要的意义。本文提出一种基于深度学习的双目内窥镜三维测量方法,可以克服传统双目匹配算法在弱纹理区域鲁棒性较差的缺点。利用三维扫描仪获得的目标三维数据和三维渲染软件仿真的双目相机创建虚拟双目图像数据集,用于训练视差预测模型进行三维测量。实验结果表明,所提方法相比传统双目匹配算法在视差准确度和视差图生成速度上分别提高48.9%和90.5%,能够提供更加准确、可靠的病灶尺寸信息,提高内窥镜诊断效率。

关键词:双目内窥镜;三维测量;深度学习;视差预测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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