Spatially Constrained and Deeply Learned Bilateral Structural Intensity-Depth Registration Autonomously Navigates a Flexible Endoscope

Abstract

Endoscope tracking is commonly utilized to provide surgeons with in-body camera poses and visual fields during invasive procedures. The fundamental aspect of endoscopic navigation lies in precisely and continuously tracing the position and orientation of the endoscope within monocular endoscopic video sequences in a preoperative data space. This work proposes a new spatially constrained and deeply learned bilateral structural intensity-depth 2D-3D registration framework for autonomously navigating a flexible endoscope. Concretely, a novel bilateral structural intensity-depth similarity function is defined to tackle the deficiency of using image intensity, while a cross-domain monocular depth estimation model trained on virtual image data is used to accurately predict real image dense depth. Additionally, a spatial constraint is introduced to precisely reinitialize an optimizer to reduce accumulative tracking errors. We validate our method on clinical data, with the experimental results showing that our method significantly outperforms current vision-based navigation methods. Particularly, the average of position and orientation errors were reduced from (4.59mm, 9.22°) to (1.65mm, 4.67°).