Granted Patent B2

US 12,318,150 · App. 18/045,474 · Granted Jun 3, 2025

Inventors: Thomas Calloway (Pelham, NH); Sanjay Joshi (Andover, MA); Tushar Sawant (Newton, MA); Rand Kmiec (Nashua, NH); Norbert Johnson (North Andover, MA)

Assignee: Globus Medical Inc.

A61B34/20A61B90/361G06T7/85A61B2034/2055A61B2090/3937A61B2090/3983G06T2207/10028G06T2207/20081G06T2207/30004G06T2207/30204G06T2207/30244

Abstract

Claims (19)

1. A camera tracking system for computer assisted navigation during surgery, comprising at least one processor operative to:

identify locations of markers of a reference array in a set of the images obtained from tracking cameras imaging a real device with at least partially overlapping field-of-views;

determine measured coordinate locations of a feature of a real device in the set of the images based on the identified locations of the markers and based on a relative location relationship between the markers and the feature;

process a region of interest in the set of the images identified based on the measured coordinate locations through a neural network configured to output a prediction of coordinate locations of the feature in the set of the images, wherein the neural network has been trained based on training images containing the feature of a computer model rendered at known coordinate locations;

track pose of the feature of the real device in three-dimensional (3D) space based on the prediction of coordinate locations of the feature of the real device in the set of the images;

determine a predicted 3D pose of the feature of the real device in a tracked space based on triangulation of the prediction of two-dimensional (2D) coordinate locations of the feature of the real device in a pair of the set of the images from a pair of the tracking cameras;

determine a measured 3D pose of the feature of the real device in the tracked spaced based on triangulation of the locations of the markers of the reference array in the pair of the set of the images; and

calibrate a feature offset based on comparison of the predicted 3D pose of the feature and the measured 3D pose of the feature.

2. The camera tracking system of claim 1 , wherein:

the prediction of coordinate locations of the feature of the real device indicates predicted pixel coordinates of the feature within the set of the images.

3. The camera tracking system of claim 1 , wherein the at least one processor is further operative to:

verify the feature of the real device based on whether the measured coordinate locations of the feature of the real device are within a threshold distance of the prediction of coordinate locations of the feature of the real device.

4. The camera tracking system of claim 3 , wherein the at least one processor is further operative to:

process training images, which contain the feature of the computer model rendered at the known coordinate locations in the training images, through the neural network to output predictions of coordinate locations of the feature of the computer model in the training images;

compare the known coordinate locations of the feature in the training images to the predictions of coordinate locations of the feature of the computer model in the training images; and

train parameters of the neural network based on the comparison.

5. The camera tracking system of claim 4 , wherein the at least one processor is further operative to:

generate the training images containing the feature of the computer model rendered at the known coordinate locations in the training images with different rendered backgrounds, different rendered lighting conditions, and/or different rendered poses of the feature of the computer model between at least some of the training images.

6. The camera tracking system of claim 4 , wherein training parameters of the neural network comprises to adapt weights and/or firing thresholds assigned to combining nodes of at least one layer of the neural network, based on the training images containing the feature of the computer model rendered at the known coordinate locations in the training images.

Assignments (1)

Continuity (1)

References Cited (400)