Multistage adaptive control strategy based on image contour data for autonomous endoscope navigation

The physician burnout, poor ergonomics are hardly conducive to the sustainability and high quality of colo-noscopy. In order to reduce doctors' workload and improve patients' experiences during colonoscopy, this paper proposes a multistage adaptive control approach based on image contour data to guide the autonomous navi-gation of endoscopes. First, a fast image preprocessing and contour extraction algorithms are designed. Second, different processing algorithms are developed according to the different contour information that can be clearly extracted to compute the endoscope control parameters. Third, when a clear contour cannot be extracted, a triple control method inspired by the turning of a novice car driver is devised to help the endoscope capture clear contours. The proposed multistage adaptive control approach is tested in an intestinal model over a variety of curved configurations and verified on the actual colonoscopy image. The results reveal the success of the strategy in both straight sections of this intestinal model and in tightly curved sections as small as 6 cm in radius of curvature. In the experiment, processing time for a single image is 20-25 ms and the accuracy of judging steering based on intestinal model pictures is 96.7%. Additionally, the average velocity reaches 3.04 cm/s in straight sections and 2.49 cm/s in curved sections respectively.

Automated summary

This paper proposes a multistage adaptive control approach based on image contour data to improve the effectiveness of colonoscopy. By designing fast image preprocessing and contour extraction algorithms, and developing different processing algorithms based on extracted contour information, the endoscope navigation is successfully guided in an intestinal model. The experimental results demonstrate the effectiveness of this approach in both straight and curved sections.