Fast, robust, and accurate monocular peer-to-peer tracking for surgical navigation

Abstract

Purpose This work presents a new monocular peer-to-peer tracking concept overcoming the distinction between tracking tools and tracked tools for optical navigation systems. A marker model concept based on marker triplets combined with a fast and robust algorithm for assigning image feature points to the corresponding markers of the tracker is introduced. Also included is a new and fast algorithm for pose estimation. Methods A peer-to-peer tracker consists of seven markers, which can be tracked by other peers, and one camera which is used to track the position and orientation of other peers. The special marker layout enables a fast and robust algorithm for assigning image feature points to the correct markers. The iterative pose estimation algorithm is based on point-to-line matching with Lagrange-Newton optimization and does not rely on initial guesses. Uniformly distributed quaternions in 4D (the vertices of a hexacosichora) are used as starting points and always provide the global minimum. Results Experiments have shown that the marker assignment algorithm robustly assigns image feature points to the correct markers even under challenging conditions. The pose estimation algorithm works fast, robustly and always finds the correct pose of the trackers. Image processing, marker assignment, and pose estimation for two trackers are handled in less than 18 ms on an Intel i7-6700 desktop computer at 3.4 GHz. ConclusionThe new peer-to-peer tracking concept is a valuable approach to a decentralized navigation system that offers more freedom in the operating room while providing accurate, fast, and robust results.

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

“…Optical tracking tools use an illuminator and passive marker spheres with unique retro-reflective surfaces that can be attached to any target and detected by the illuminator. Typically, the OTS will assign a frame of reference to facilitate calibration between devices’ coordinates and perform image-to-target registration [ 27 , 29 , 31 , 69 ]. By generating a defined EM field in which EM micro-sensors are tracked, rigid, and flexible, medical instruments embedded within these sensors can be tracked without obstruction.…”

Electrode-free visual prosthesis/exoskeleton control using augmented reality glasses in a first proof-of-technical-concept study

“…As mentioned previously, our system does not suffer from drifting errors, such as those in known IMU-based systems, because each tracking position is optimized independently, as described in 18 .…”

“…The tracker on the prosthesis consisted of seven conventional 770 nm infrared LEDs (MTE1077M3A-R) located in one plane and arranged in specific triplet configurations, as described in 18 . Infrared LEDs together with an infrared filter in front of the built-in camera simplified the search for LED blobs in the camera image.…”

“…These blobs were found using a connected-component labeling algorithm for blob detection, and the centroid of each LED was determined by a weighted centroid calculation, which resulted in precise subpixel-scale coordinates. After assigning the LED blobs to the corresponding LEDs of the real tracking tool, the pose estimation algorithm described in 18 was used to obtain the pose of the prosthesis with respect to the camera on the AR glasses.…”