Abstract:
The present disclosure relates to calibration target devices, assemblies and methods for use with imaging systems, such as a stereoscopic endoscope. A calibration assembly includes: a target surface extends in three dimensions with calibration markers and a body with an interface that engages an endoscope so the markers are within the field of view. A first calibration marker extends along a first plane of the target surface and a second marker extends along a second plane of the target surface. The planes are different and asymmetric relative to the field of view as seen through the endoscope. Three-dimensional targets, in particular, enable endoscopic calibration using a single image (or pair of images for a stereoscopic endoscope) to reduce the calibration process complexity, calibration time and chance of error as well as allow the efficient calibration of cameras at different focus positions.
Abstract:
In a minimally invasive surgical system, a hand tracking system tracks a location of a sensor element mounted on part of a human hand. A system control parameter is generated based on the location of the part of the human hand. Operation of the minimally invasive surgical system is controlled using the system control parameter. Thus, the minimally invasive surgical system includes a hand tracking system. The hand tracking system tracks a location of part of a human hand. A controller coupled to the hand tracking system converts the location to a system control parameter, and injects into the minimally invasive surgical system a command based on the system control parameter.
Abstract:
A medical robotic system and method of operating such comprises taking intraoperative external image data of a patient anatomy, and using that image data to generate a modeling adjustment for a control system of the medical robotic system (e.g., updating anatomic model and/or refining instrument registration), and/or adjust a procedure control aspect (e.g., regulating substance or therapy delivery, improving targeting, and/or tracking performance).
Abstract:
A medical tracking system comprises a fiducial apparatus that includes a sensor docking feature configured to mate with a mating portion of a sensor device. The sensor docking feature retains the mating portion in a known configuration. The fiducial apparatus also includes at least one imageable fiducial marker and a surface configured for attachment to an anatomy of a patient.
Abstract:
Information extracted from sequential images captured from the perspective of a distal end of a medical device moving through an anatomical structure are compared with corresponding information extracted from a computer model of the anatomical structure. A most likely match between the information extracted from the sequential images and the corresponding information extracted from the computer model is then determined using probabilities associated with a set of potential matches so as to register the computer model of the anatomical structure to the medical device and thereby determine the lumen of the anatomical structure which the medical device is currently in. Sensor information may be used to limit the set of potential matches. Feature attributes associated with the sequence of images and the set of potential matches may be quantitatively compared as part of the determination of the most likely match.
Abstract:
A robotic system comprises an input device movable by an operator and a processing unit. An operator reference frame is defined relative to the operator. The processing unit is configured to present, to the operator, a first image of a first tool captured by an imaging device, receive, from the operator, a first indication that a first axis of the input device is aligned with a corresponding axis of the first tool in the first image, and in response to the first indication, determine a first alignment relationship between the imaging device and the first tool based on a second alignment relationship between the operator reference frame and the input device.
Abstract:
Systems and methods for supporting image-guided procedures include a device having an instrument usable to collect location data for one or more passageways and one or more processors coupled to the instrument. The one or more processors are configured to organize a plurality of points within the location data based on a corresponding insertion depth of the instrument when each of the plurality of points is collected, create a passageway tree based on the points, identify at least three non-collinear landmark locations within the passageway tree, create a seed transformation between one or more of the at least three non-collinear landmark locations and corresponding model locations in model data, and register, using the seed transformation, the plurality of points to the model data for the one or more passageways. In some embodiments, the at least three non-collinear landmark locations are based on a main branch point in the passageway tree.
Abstract:
A medical robotic system and method of operating such comprises taking intraoperative external image data of a patient anatomy, and using that image data to generate a modeling adjustment for a control system of the medical robotic system (e.g., updating anatomic model and/or refining instrument registration), and/or adjust a procedure control aspect (e.g., regulating substance or therapy delivery, improving targeting, and/or tracking performance).
Abstract:
A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting at least one of a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The weighting that at least one of the plurality of volumetric units includes applying a weight based on a foreground structure classification. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.
Abstract:
A method includes receiving anatomic image data comprising a plurality of graphical units; generating an initial segmented set of the plurality of graphical units associated with an anatomic tree structure; receiving an indication of a first user selected graphical unit, the first user selected graphical unit being outside the initial segmented set of the plurality of graphical units; applying a cost function to determine a first sequence of graphical units from the first user selected graphical unit to the initial segmented set of graphical units; receiving an indication of a second user selected graphical unit, the second user selected graphical unit being selected from the first sequence of the plurality of graphical units; and applying the cost function to determine a second sequence of the plurality of graphical units from the second user selected graphical unit to the initial segmented set of the plurality of graphical units.