Dense depth estimation in monocular endoscopy with self-supervised learning methods

Xingtong Li, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter, Russell H. Taylor, Mathias Unberath

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Feb 20 2019


  • Depth estimation
  • Endoscopy
  • Self-supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • General

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