Patent Description:
Navigated surgery in the orthopedic domain often requires registration of a reconstructed image of a bone surface to a co-modality image, such as computed tomography (CT) or magnetic resonance imaging (MRI) scan. Conventional techniques established in the clinical routine usually reconstruct the image solely on an area of the bone surface contacted with a tracked pointer following exposure at the surgical site. As an alternative, ultrasound is becoming increasingly used in navigated surgery and registration-based applications to image and reconstruct a larger area of the bone surface. However, spatial and temporal information quality of ultrasound is relatively inferior to other modalities primary due to speed-of-sound variations and refractions, and tissue deformation. Further challenges are associated with many conventional tracked ultrasound systems, as they often require a custom image processing algorithm to detect the bone surface in individual ultrasound frames. These systems and methods have severe limitations with either insufficient detection coverage of the visible bone surface, or the occurrence of false positive detections.

Therefore, a need exists in the art for a navigated surgery system using ultrasound with an improved workflow to register ultrasound imaging to a co-modality imaging that overcomes one or more of the aforementioned disadvantages.

Non-patent literature <NPL>, discloses techniques for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a bone-specific feature descriptor. Further, a global optimization strategy aligns the bone surface, followed by a soft tissue aware intensitybased registration.

Document <CIT> discloses an ultrasonic imaging device, and an image processing device and a method utilized for the aim of achieving automatic and reliable surgery navigation by simultaneously displaying: the name and positional relation of a distinctive site inside a subject; ultrasonic images; and CT cross section images corresponding to the ultrasonic images. For this, ultrasonic volume data for an ultrasonic image and CT volume data acquired by a different image-capturing device is received, and a plurality of prescribed distinctive sites of a subject is estimated and identified from the ultrasonic volume data and the CT volume data. A CT volume coordinate conversion calculating unit calculates coordinate conversion information for alignment using the positional information of the distinctive sites corresponding to the ultrasonic volume data and the CT volume data.

This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description below. This Summary is not intended to limit the scope of the claimed subject matter nor identify key features or essential features of the claimed subject matter.

According to aspects of the present disclosure, a computer-implemented method for automatically registering intra-operative ultrasound imaging of an object with a pre-operative co-modality imaging of the object, and a system for automatically registering intra-operative ultrasound imaging of an object with a pre-operative co-modality imaging of the object are provided according to the independent claims. Preferred embodiments are recited in the dependent claims.

The techniques described herein advantageously provide a workflow to accurately register ultrasound imaging to a co-modality imaging based on an automatic and real-time image segmentation and calibration. A convolutional neural network (CNN) provides fast, robust, and accurate bone segmentation of the ultrasound imaging in real-time. The real-time detection allows for: (i) automatic speed-of-sound analysis; (ii) compensation methods based on steered compound ultrasound imaging around the detected bone; (iii) automatic temporal calibration between a tracking coordinate system and the ultrasound imaging; and (iv) automatic adaptation of focus, depth and frequency of the ultrasound imaging. Techniques based on deep learning overcome the aforementioned challenges and produce accurate bone probability maps in a more robust way that standard feature-based methods. Examples provided advantageously result in overall errors of less than one millimeter.

The system and methods may exhibit advantages and provide technical solutions other than those described herein.

Other aspects, features, and advantages of the present invention will become more fully apparent from the detailed description, the appended claims, and the accompanying drawings wherein like reference numerals identify similar or identical elements.

<FIG> shows an embodiment of a surgical navigation system <NUM> for use with embodiments of the workflow to be described. The surgical navigation system <NUM> is shown in a surgical setting, such as an operating room of a medical facility, and arranged to track movement of various objects in the operating room. Such objects include, for example, an ultrasound device <NUM>, a surgical instrument <NUM>, and trackers <NUM>, <NUM> coupled to, for example, a femur F and a tibia T of a patient. In the illustrated example, the ultrasound device <NUM> is a handheld probe movable in proximity to the anatomy of interest. Alternatively, the ultrasound device <NUM> may be attached to a robotic arm. One or more trackers <NUM> may be coupled to the ultrasound device <NUM>. A tracker <NUM> may also be coupled to the surgical instrument <NUM>. The trackers <NUM>, <NUM> may be attached to the femur F in the manner shown in <CIT>, entitled Surgery System. Optical sensors <NUM> of the localizer <NUM>, for example, three separate high resolution charge-coupled devices (CCD) to detect infrared (IR) signals, track the trackers <NUM>, <NUM>, <NUM>, <NUM>. A camera unit <NUM> includes a camera controller <NUM> in communication with the optical sensors <NUM> to receive signals from the optical sensors <NUM>. The camera unit <NUM> receives optical signals from the ultrasound device <NUM> and the trackers <NUM>, <NUM>, <NUM>, <NUM>. The camera unit <NUM> outputs to the processor <NUM> signals relating to the position of the ultrasound device <NUM> and the trackers <NUM>, <NUM>, <NUM>, <NUM> relative to the localizer <NUM>. Position and orientation signals and/or data are transmitted to the navigation computer <NUM> for purposes of tracking the objects. A navigation computer <NUM> includes a navigation interface having one or more displays <NUM>, <NUM> and input devices <NUM>, <NUM> for inputting information into the navigation computer <NUM> or otherwise select or control certain aspects of the navigation computer <NUM>. The navigation computer <NUM> also includes the processor <NUM> such as one or more microprocessors or multi-processor systems to control operation
of the navigation computer <NUM>. The navigation system <NUM> communicates position and/or orientation data to the robotic control system. The position and/or orientation data is indicative of a position and/or orientation of the surgical instrument <NUM> relative to the anatomy. This communication provides closed loop control to control cutting of the anatomy such that the cutting occurs within a predefined path or anatomical boundary.

In the embodiment shown in <FIG>, the surgical instrument <NUM> is an end effector of a surgical manipulator similar to that described in <CIT>, entitled "Navigation System for use with a Surgical Manipulator Operable in Manual or Semi-Autonomous Modes," and <CIT> entitled, "Navigation System for Removing a Volume of Tissue from a Patient". The robotic surgical system may include the manipulator including a plurality of arms and the surgical instrument <NUM> carried by at least one of the plurality of arms. A robotic control system controls or constrains movement of the cutting tool in at least five degrees of freedom.

In other embodiments, the surgical instrument <NUM> may be manually positioned by only the hand of the user, without the aid of any cutting guide, jib, or other constraining mechanism such as a manipulator or robot. One such suitable surgical instrument, for example, is described in <CIT>, entitled, "Surgical Instrument Including Housing, a Cutting Accessory that Extends from the Housing and Actuators that Establish the Position of the Cutting Accessory Relative to the Housing,". In other systems, the instrument <NUM> has a cutting tool that is movable in three degrees of freedom relative to a handheld housing and is manually positioned by the hand of the surgeon, without the aid of cutting jig, guide arm or other constraining mechanism. Such systems are shown in <CIT>, entitled, "Surgical Instrument Including Housing, a Cutting Accessory that.

Extends from the Housing and Actuators that Establish the Position of the Cutting Accessory Relative to the Housing,". The system includes a hand held surgical cutting instrument having a cutting tool. A control system controls movement of the cutting tool in at least three degrees of freedom using internal actuators/motors. In such an embodiment, the navigation system <NUM> communicates with the control system of the hand held surgical cutting instrument. The navigation system <NUM> communicates position and/or orientation data to the control system. The position and/or orientation data is indicative of a position and/or orientation of the instrument <NUM> relative to the anatomy. The position and/or orientation data is more accurately determined with the workflow <NUM> as described throughout the present disclosure. This communication provides closed loop control to control cutting of the anatomy such that the cutting occurs within a predefined boundary (the term predefined boundary includes predefined trajectory, volume, line, other shapes or geometric forms, and the like).

The workflow and techniques can be utilized with various other techniques relating to ad-hoc intraoperative surgical planning, such as those techniques described in <CIT>, entitled "Computer Aided Planning of Orthopaedic Surgeries,".

When tracking both the instrument <NUM> and the anatomy being cut in real time in these systems, the need to rigidly fix anatomy in position can be eliminated. Since both the surgical instrument <NUM> and anatomy are tracked, control of the surgical instrument <NUM> can be adjusted based on relative position and/or orientation of the surgical instrument <NUM> to the anatomy. Also, representations of the surgical instrument <NUM> and anatomy on the display(s) <NUM>, <NUM> can move relative to one another - to emulate their real world motion. In one embodiment, each of the femur F and tibia T has a target volume of material that is to be removed by the working end of the
surgical instrument <NUM>. The target volumes are defined by one or more boundaries. The boundaries define the surfaces of the bone that should remain after the procedure. In some embodiments, the system <NUM> tracks and controls the surgical instrument <NUM> to ensure that working end, e.g., bur, only removes the target volume of material and does not extend beyond the boundary, as disclosed in <CIT> entitled, "System and Method for Providing Substantially Stable Control of a Surgical Tool". Control of the surgical instrument <NUM> may be accomplished by utilizing, at least in part, the registered intraoperative ultrasound image to the pre-operative imaging with the workflow <NUM> to be described. With the improved registration, the surgical instrument <NUM> or the manipulator to which it is mounted, may be controlled so that only desired material is removed.

Any aspects of the workflow <NUM> described herein can be executed on any of the computers, or controllers described herein. The computers or controllers can comprise a non-transitory computer-readable medium, having instructions stored therein. When the instructions are executed by one or more processor, the instructions implement any aspect of the workflow.

Co-modality imaging of the femur F and tibia T (or of other tissues in other embodiments) is acquired. The co-modality imaging may be based on CT scans, MRI scans, radiological scans, or other suitable imaging of the patient's anatomy of interest. The co-modality imaging is acquired preoperatively.

The ultrasound imaging includes propagating ultrasonic waves along a plurality of scanlines through the object or anatomy of interest. The incident waves are reflected from objects or anatomy of differing characteristics with the reflected waves detectable by the ultrasound device <NUM>. The reflected waves may be processed by the navigation system <NUM> to generate frames that are combined to display in real time the ultrasound imaging. In one example, the ultrasound device <NUM> is the aforementioned hand held probe is moved relative the anatomy of interest to perform a "sweep. " The ultrasound imaging is obtained intraoperatively.

<FIG> is a flowchart of a sample workflow <NUM> to be described to provide for automatic, accurate, and/or real-time segmentation and calibration of the ultrasonic imaging for registration to the co-modality imaging. The workflow <NUM> includes the steps of segmenting the ultrasound imaging (step <NUM>), calibrating for variations in the speed of sound (step <NUM>), calibrating for temporal lag (step <NUM>), and registering the ultrasound imaging to co-modality imaging (step <NUM>). Steps <NUM>-<NUM> can be performed in a sequence other than the sequence shown in <FIG>. Moreover, steps <NUM>-<NUM> provide independent technical solutions and can be performed independently or with workflows other than the workflow shown in <FIG>.

At step <NUM>, the object surface captured in the ultrasound imaging is detected and segmented. The workflow <NUM> includes a surface detection algorithm using the convolutional neural network (CNN), and in certain embodiments a fully convolutional neural network (f-CNN). In other words, the ultrasound imaging is segmented with the CNN to detect a surface of the object, for example, a surface of a bone. The f-CNN is initially trained on a set of labeled images having the bone area roughly drawn by several users. In one example, the f-CNN is trained by defining multi-resolution layer combinations, such as semantic information from a deep, coarse layer with appearance information from a shallow, fine layer. The resulting f-CNN is suitably trained to engage in bone surface recognition and segmentation.

During the operative procedure, the ultrasound imaging may be processed and provided as an input image to the navigation processor <NUM>. For example, with reference to <FIG>, the ultrasound device <NUM> may be moved to a position adjacent to and in contact with the patient such that the ultrasound device <NUM> detects the femur F or the tibia T of the patient. A sweep of the femur F or the tibia T may be performed.

The workflow includes the f-CNN analyzing the input image. In one example, the f-CNN includes a series of <NUM>×<NUM> convolutional layers with ReLU non-linearities and maxpooling layers, followed by deconvolutional layers and similar non-linearities. The f-CNN generates a probability map (step <NUM>). Each of <FIG> shows an ultrasonic image <NUM>a-c juxtaposed against the probability map <NUM>a-c of the object <NUM> predicted by the f-CNN. The probability map <NUM>a-c of the bone, at this point, may be considered generally "fuzzy. " In illustrated embodiment shows the probability map <NUM>a-c being the same size as the input image.

The workflow <NUM> includes the step of extracting the bone surface from the probability map <NUM>a-c (step <NUM>). In certain embodiments, the bone surface may be extracted for each scanline from the imaging. For example, the probability map <NUM>a-c of the bone may include a maximum gradient and a maximum intensity along the scanline. The bone surface may be the center pixel between the maximum gradient and the maximum intensity along the scanline. Based on the reliability of the f-CNN, which was previously trained for deep, feed-forward processing, using only the maximum gradient and the maximum intensity is sufficient to identify the bone surface. Consequently, most outliers that may negative affect the analysis are discarded. Further, the simplicity associated with the f-CNN and using thresholding (i.e., the maximum gradient) and largest component (i.e., the maximum intensity) analysis, the workflow <NUM> is able to be performed in real-time, for example, at thirty images per second. In other words, the bone surface captured with ultrasound image may be detected (i.e., identified and segmented) in real-time with improvement in the speed with which the workflow <NUM> is performed. Furthermore, dedicated online algorithms may be leveraged during the workflow <NUM>.

In certain instances, for example delay-sum beamforming, misalignment when imaging an object from different angles (e.g., during a sweep) results from the speed of sound expanding or compressing the images along the direction of beams. The problem may be particularly pronounced for an overlapping area of steered frames (i.e., an ultrasound frame directed at the object from different angles that results in the overlapping area). <FIG> demonstrates effect of speed of sound on overlapping area <NUM> of steered frames <NUM>a-b, namely with the speed of sound being underestimated by ten percent, correct, and overestimated by ten percent. The workflow <NUM> of the present disclosure advantageously provides for speed-of-sound analysis and speed-of-sound calibration (step <NUM>) to reflect variation in propagation speed of the ultrasound waves through the object. More particularly, the workflow <NUM> compensates for any expansion or compression the images along the direction of beams, thereby producing more accurate imaging prior to registration with the co-modality imaging. The speed-of-sound calibration may be provided in real-time.

The workflow <NUM> includes summing the differences between a first steered frame <NUM>a and a second steered frame <NUM>b of the ultrasound imaging (step <NUM>), and estimating the propagation speed (step <NUM>). The propagation speed is estimated by optimizing the appearance of first and second steered frames <NUM>a-b. Given the first and second steered frames I and J, respectively, the propagation speed c may include minimizing following cost function (step <NUM>) to optimize the sum of the differences between the first and second steered frames:
<MAT>
where S is the set of all pixels within the bone region of interest in image Ic; Ipc and Jpc are corresponding pixel intensities in the images after compounding them with the speed of sound c. It is noted that more advanced measures of similarity do not significant alter the results.

The workflow <NUM> addresses challenges with comparing frames of ultrasound imaging due to reflection from most tissue boundaries and objects depending on the insonification angle. The workflow <NUM> provides consistency to the calibrated or optimized value of the propagation speed. Referring to <FIG>, non-bone areas <NUM> are masked out (right), then each of the first and second steered frames <NUM>a-b (Il and Ir) are compared to a third frame of the ultrasound imaging that is angled between the first and second steered frames. The third frame is a perpendicular image (Im). More specifically, a first difference between the first steered frame and the third frame is computed, and a second difference between the second steered frame and the third frame is computed. The calibrated or optimized value for the propagation speed is calculated as
<MAT>.

<FIG> each shows two point clouds <NUM>, <NUM> each extracted from ultrasound sweeps acquired at a different angle with the point clouds <NUM>, <NUM> superimposed on the ultrasound imaging. <FIG> illustrates that prior to calibration, the point clouds <NUM>, <NUM> are less in alignment with the object of the ultrasound imaging based on, for example, the speed of sound expanding or compressing and its effect of speed of sound on overlapping area <NUM> of steered frames <NUM>a-b. <FIG> illustrates that following calibration, improved shape consistency is shown between the point clouds <NUM>, <NUM> and the object. <FIG> plots the cost function relative to the speed of sound for estimations prior to and after calibration, i.e., <FIG>, respectively. <FIG> shows that the cost function is at a global minimum following calibration for speed of sound using the aforementioned workflow <NUM>. Estimating the speed of sound with two frames facilitates real-time calibration with leveraging online algorithms, and further allows the workflow to be processed in real-time. Conventional methods directly comparing the two steered frames increases dissimilarities in the point-spread-function, and conventional methods requiring additional steered frames requires computing and other resources less suitable for real-time processing and leveraging of online algorithms.

As mentioned, the position of the ultrasound device <NUM> can be tracked by the surgical navigation system <NUM>, for example, by the optical sensors <NUM> of the localizer <NUM> sensing the trackers <NUM> coupled to the ultrasound device <NUM>. Certain inaccuracies may be associated with such systems tracking a moving probe that is concurrently obtaining intraoperative ultrasound imaging. Thus, the workflow <NUM> includes spatial and temporal calibration of the ultrasound imaging (step <NUM>), and in particular to determine the relative transformation between the ultrasound device <NUM> and image coordinates as detected by the ultrasound device <NUM> (e.g., the coordinates of the segmented bone surface as adjusted for variances in the speed of sound). The automatic spatial calibration - i.e., adaptation of focus, depth and frequency of the intraoperative ultrasound imaging may include non-linear optimization that maximizes the two-dimensional ultrasound imaging from one sweep to reconstructions computed from the other sweep. Extensions to the image-based spatial calibration may be included to optimize over multiple recordings and handle more arbitrary sweep geometries.

Referring now to <FIG>, the temporal calibration (e.g., a temporal synchronization shift parameter) may be optimized separately. Embodiments of the workflow <NUM> include recording an ultrasound sweep from the bone surface while the ultrasound device <NUM> is slowly pushed towards the object and released several times. <FIG> illustrate a reconstruction of a sweep recorded with the ultrasound device <NUM> being slowly pushed towards the bone and released several times. The sweeps in shown in <FIG> are expanded along the object <NUM> to better visualize the motion with sweeps moving perpendicular to the bone only used for calibration. The f-CNN identifies and segments the bone surface in the manner previously described (step <NUM>). A point cloud is extracted considering the tracking information of each frame. In other words, a point cloud is created (step <NUM>) based on the tracked position of the ultrasonic probe <NUM> tracked relative to, for example, the localizer <NUM> of the surgical navigation system <NUM>. A set of values is created or calculated by projecting the point cloud to a vector (step <NUM>). In one example, the vector is a three-dimensional vector oriented parallel to the average direction of ultrasound scanlines.

<FIG> shows a reconstruction of an object <NUM> for an ultrasound sweep before optimization, and <FIG> shows a reconstruction of the object <NUM> after optimization. The temporal lag is computed (step <NUM>) by choosing a value which minimizes variance of the set of values and provides the cost function at a global minimum at the optimization of the temporal lag, as shown in <FIG> (right). In one example, the standard deviation of repeated calibrations on recorded sweeps from human femur is two milliseconds. Temporal calibration is adapted to be performed automatically in real-time by, for example, the processor <NUM> of the navigation system <NUM>.

With the workflow <NUM> as described above, superior spatial and temporal information quality in ultrasound is achieved for registration to the co-modality imaging. Subsequent to the real-time bone identification and segmentation, speed-of-sound calibration, and spatial and temporal calibration, the ultrasound imaging is automatically registered to the co-modality imaging (step <NUM>). In certain embodiments, the registration is formulated as a point-to-surface distance minimization problem. More specifically, the sum of the absolute distance of all points extracted from the calibrated intraoperative ultrasound image to the pre-operative image segmented bone surface are minimized. This point-to-surface distance minimization problem may
be solved via a global optimizer to avoid local minima and allow for automatic initialization during surgery. One suitable global optimizer may be DiRect as disclosed in <NPL>. In one example, a bounding search space was set to [-<NUM>; <NUM>] for translations and [-<NUM>∘; <NUM>∘] for rotations. In certain embodiments, a signed distance transform from the CT image segmentation may be precomputed for faster processing and evaluation. Once the global optimizer has found a suitable minimum, the transformation estimate is refined with a more local optimizer after removing the outliers - points which are further away than a predetermined distance after the first registration (e.g., five millimeters). One suitable local optimizer may be the Nelder-Mead method as described in <NPL>. Similar to the intraoperative ultrasound imaging, the pre-operative image may require segmentation prior to registration. The CT image segmentation may be performed automatically or manually. Further, the CT image segmentation, particularly when performed manually, may be refined to provide a voxel-wise accurate bone surface, which may not be present when axial slices based on manual segmentations are stacked in three dimensions. In certain embodiments, the image segmentation may be refined using a three-dimensional guided filter to perform fast and precise image matting in three dimensions, and the bone surface may be extracted using a marching cubes algorithm.

A 4x4 wire grid was created on Perklab fCal <NUM> phantom and immersed in a water tank with <NUM>∘C temperature. Based on the temperature and salinity of the water, expected
sound speed was <NUM> milliseconds (ms). Three ultrasound imaging with -<NUM>°, <NUM>°, and +<NUM>° angles were recorded from the wire grid with imaging depth of <NUM> centimeters (cm). Wires were positioned with <NUM> spacing at depth of <NUM> to <NUM>. Speed of sound was set to <NUM> on ultrasound machine. Then the speed of sound calibration was performed on the steered frames (bone surface masking step was ignored in this experiment). The result value was <NUM>, which is <NUM>% error.

A CT scan of the bilateral lower extremities of two cadavers were acquired after implanting six multi-modal spherical fiducials into each of the bones of interest, namely pelvis, femur, and tibia. For recording freehand ultrasound sweeps an optical tracking camera was used with a reference tracking target fixed to the bone and another target on the ultrasound device <NUM>. One-hundred forty-two tracked ultrasound sweeps were recorded by two orthopedic surgeons. Ultrasound imaging were recorded with different frame geometries, image enhancement filters, brightness, and dynamic contrast to assure that ultrasound bone detection algorithm does not overfit to a specific bone appearance. Scan geometries consisted of linear, trapezoid, and steered compound images with <NUM> consecutive frames (-<NUM>°/<NUM>°/+<NUM>° or -<NUM>°/<NUM>°/+<NUM>°). For the steered sweeps, the three original steered frames (subframes), in order to perform online speed of sound calibration. All ultrasound imaging were recorded with a linear <NUM>-element probe at <NUM> center frequency on a Cephasonics cQuest Cicada system.

In order to generate a ground truth registration between the CT images and the US sweeps, the fiducials were also scanned just before the ultrasound sweep acquisition, by accessing them with a dedicated hollow halfsphere fiducial tracking pointer. On the CT scans, the fiducial positions were extracted with an automatic algorithm based on a sub-voxel accurate sphere
fitting. Point-correspondence registration then yielded a rigid transformation which is our Ground Truth. We obtained sub-mm accuracy in ground truth registration error (defined as the average residual distance between the CT and tracked fiducial positions after registration) with an average of <NUM>, and a median of <NUM>.

The references to methods of treatment by surgery in this paragraph are to be interpreted as references to the surgical navigation system for use in those methods. With segmented and calibrated ultrasound image registered with the pre-operative image, the surgical navigation system <NUM> may proceed with the surgical procedure. In one embodiment, the navigation system is part of a robotic surgical system for treating tissue (see <FIG>). In one example, a cutting system of the robotic surgical system is used to prepare bone for surgical implants such as hip and knee implants, including unicompartmental, bicompartmental, or total knee implants. Some of these types of implants are shown in <CIT> entitled, "Prosthetic Implant and Method of Implantation".

Claim 1:
A computer-implemented method for automatically registering intraoperative ultrasound imaging of an object with a pre-operative co-modality imaging of the object, wherein the ultrasound imaging is generated by propagating ultrasound waves along a plurality of scanlines through the object, the computer-implemented method comprising:
segmenting (<NUM>) the ultrasound imaging with a convolutional neural network "CNN" to detect a surface of the object;
calibrating (<NUM>) the ultrasound imaging to reflect a variation in propagation speed of the ultrasound waves through the object based on an estimated propagation speed, wherein estimating the propagation speed includes:
comparing first and second steered frames (66a, 66b) of the ultrasound imaging with a third frame of the ultrasound imaging that is perpendicular to the first and second steered frames (66a, 66b by computing a first absolute difference between pixel intensities of the first steered frame (66a) and the third frame, and computing a second absolute difference between pixel intensities of the second steered frame (66b) and the third frame, wherein the pixel intensities are compounded with a propagation speed;
computing (<NUM>) a sum of the first and second absolute differences;
optimizing (<NUM>) the sum by varying the compounded propagation speed to minimize a cost function, the cost function being defined as the sum of the first and second absolute differences per absolute set of all pixels within the object region in the ultrasound imaging; and
estimating (<NUM>) the propagation speed of the ultrasound waves through the object based on the optimized sum;
temporally calibrating (<NUM>) the ultrasound imaging with respect to a tracking coordinate system by:
creating (<NUM>) a point cloud of the surface based on a tracked position of an ultrasound probe (<NUM>);
calculating (<NUM>) a set of projection values of the point cloud to a 3D vector that is oriented parallel to an average direction of the plurality of scanlines; and
calculating (<NUM>) a temporal lag for the ultrasound imaging based on the set of projection values comprising choosing a value which minimizes variance of the set of values and provides a cost function at a global minimum at the optimization of the temporal lag; and
automatically registering (<NUM>) the segmented and calibrated ultrasound imaging to the co-modality imaging.