Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods.
IEEE Transactions on Medical Imaging(2020)
摘要
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
https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch
.
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关键词
Estimation,Endoscopes,Cameras,Videos,Training,Image reconstruction,Three-dimensional displays
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