Single-shot three-dimensional reconstruction for colonoscopic image analysis

We propose a method for the three-dimensional reconstruction from a single-shot colonoscopic image. Extracting a three-dimensional colon structure is an important task in colonoscopy. However, a colonoscope captures only two-dimensional information as colonoscopic images. Therefore, an estimation of three-dimensional information from two-dimensional images has potential demands. In this paper, we integrate deep-learning-based depth estimation to three-dimensional reconstruction. This approach omits the inaccurate corresponding matching from the procedure of conventional three-dimensional reconstruction. We experimentally demonstrated accurate reconstructions with comparisons between a polyp size in three-dimensional reconstruction and an endoscopist's measurement.