A Novel Video-CTU Registration Method with Structural Point Similarity for FURS Navigation

Flexible ureteroscopy (FURS) navigation remains challenging since ureteroscopic images are poor quality with artifacts such as water and floating matters, leading to a difficulty in directly registering these images to preoperative images. This paper presents a novel 2D-3D registration method with structure point similarity for robust vision-based flexible ureteroscopic navigation without using any external positional sensors. Specifically, this new method first uses vision transformers to extract structural regions of the internal surface of the kidneys in real FURS video images and then generates virtual depth maps by the ray-casting algorithm from preoperative computed tomography urogram (CTU) images. After that, a novel similarity function without using pixel intensity is defined as an intersection of point sets from the extracted structural regions and virtual depth maps for the video-CTU registration optimization. We evaluate our video-CTU registration method on in-house ureteroscopic data acquired from the operating room, with the experimental results showing that our method attains higher accuracy than current methods. Particularly, it can reduce the position and orientation errors from (11.28 mm, 10.8 degrees) to (5.39 mm, 8.13 degrees).