Auris Health, Inc.

Menglong Ye

Ritwik Ummalaneni

Elif Ayvali

David Paul Noonan

Hedyeh Rafii-Tari

David Burdick Berman

Bulat Ibragimov

Mali Shen

Yanwei Wang

Subashini Srinivasan

Yuriy Malinin

Methods and systems provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument (e.g., an endoscope) within the tubular network. Various input data such as image data and CT data, are used to model the tubular networks, and the model information is used to generate a camera pose representing a specific site location within the tubular network and/or to determine navigation information including position and orientation for the medical instrument.

Vision-based 6DOF camera pose estimation in bronchoscopy

Anatomical feature tracking involves advancing a medical instrument to a treatment site of a patient, the medical instrument comprising an imaging device, generating a first image of at least a portion of the treatment site using the imaging device of the medical instrument when a distal end of the medical instrument is in a first position, generating a first silhouette of a first target anatomical feature represented in the first image, generating a second image of at least a portion of the treatment site using the imaging device of the medical instrument when the distal end of the medical instrument is in a second position, generating a second silhouette of a second target anatomical feature represented in the second image, and determining a target position at the treatment site based at least in part on the first silhouette and the second silhouette.

Anatomical feature tracking

A method of localizing a target anatomy involves advancing a ureteroscope to a target calyx of a kidney of a patient through at least a portion of a urinary tract of the patient, determining a positional offset between one or more position sensors associated with the ureteroscope and a target papilla of the kidney that is at least partially exposed within the target calyx, and determining a percutaneous access target based at least in part on one or more of a present position of the one or more position sensors or the positional offset.

Endoscopic anatomical feature tracking

A system includes one or more processors configured to execute instructions to cause the system to access a plurality of images generated by one or more imaging devices of an endoscope when the endoscope is disposed within anatomy of a patient, for each of the plurality of images, determine a value for each of one or more image reliability metrics, and determine a position of the endoscope within the anatomy of the patient based at least in part on the one or more image reliability metrics of each of the plurality of images.

Image reliability determination for instrument localization

Navigation of an instrument within a luminal network can include image-based airway analysis and mapping. Image-based airway analysis can include detecting one or more airways in an image captured within a luminal network and determining branching information indicative of how the current airway in which the image is captured branches into the detected 'child' airways. Image-based airway mapping can include mapping the one or more detected airways to corresponding expected airways of the luminal network in the preoperative model.

Image-based airway analysis and mapping

Processes of localizing target papillas of renal anatomy involve advancing a ureteroscope to a target calyx of a kidney of a patient through at least a portion of a urinary tract of the patient, determining a positional offset between one or more position sensors associated with the ureteroscope and a target papilla of the kidney that is at least partially exposed within the target calyx, and determining a percutaneous access target based at least in part on one or more of a present position of the one or more position sensors and the offset.

Target anatomical feature localization

Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

Robotic systems and methods for navigation of luminal network that detect physiological noise

Methods of positioning a surgical instrument can involve advancing a first medical instrument to a treatment site of a patient, the first medical instrument comprising a camera, recording a target position associated with a target anatomical feature at the treatment site, generating a first image of the treatment site using the camera of the first medical instrument, identifying the target anatomical feature in the first image using a pretrained neural network, and adjusting the target position based at least in part on a position of the identified target anatomical feature in the first image.

Anatomical feature identification and targeting

Provided are systems and methods for weight-based registration of location sensors. In one aspect, a system includes an instrument and a processor configured to provide a first set of commands to drive the instrument along a first branch of the luminal network, the first branch being outside a path to a target within a model. The processor is also configured to track a set of one or more registration parameters during the driving of the instrument along the first branch and determine that the set of registration parameters satisfy a registration criterion. The processor is further configured to determine a registration between a location sensor coordinate system and a model coordinate system based on location data received from a set of location sensors during the driving of the instrument along the first branch and a second branch.

Systems and methods for weight-based registration of location sensors

Described herein are systems and methods related to image management. A system may include an instrument that includes an elongate body and an imaging device. The elongate body can be configured to be inserted into a luminal network. The imaging device may be positioned at a distal tip of the elongate body. The system may receive from the imaging device one or more images captured when the elongate body is within the luminal network. For each of the images, the system may determine one or more metrics that are indicative of a reliability of an image for localization of the distal tip of the elongate body within the luminal network. The system may determine a reliability threshold value for each of the one or more metrics. The system can utilize the one or more images based on whether the one or more metrics meet corresponding reliability threshold values.

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Mountain View, CA, United States of America

Menglong Ye