A three-dimensional measurement method for binocular endoscopes based on deep learning

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.

Keywords

Binocular endoscope / Three-dimensional measurement / Deep learning / Disparity estimation

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2022 Zhejiang University Press