We present a self-supervised approach to training convolutional neural
networks for dense depth estimation from monocular endoscopy data without a
priori modeling of anatomy or shading. Our method only requires monocular
endoscopic videos and a multi-view stereo method, e.g., structure from motion,
to supervise learning in a sparse manner. Consequently, our method requires
neither manual labeling nor patient computed tomography (CT) scan in the
training and application phases. In a cross-patient experiment using CT scans
as groundtruth, the proposed method achieved submillimeter mean residual error.
In a comparison study to recent self-supervised depth estimation methods
designed for natural video on in vivo sinus endoscopy data, we demonstrate that
the proposed approach outperforms the previous methods by a large margin. The
source code for this work is publicly available online at
this https URL

提出了一种基于单目内窥镜数据的自监督卷积神经网络训练方法，无需解剖学或阴影的先验建模，仅需要单目内窥镜视频和多视角立体方法，如运动结构，以稀疏方式监督学习。