Monoscopic marker tracking

Abstract

The invention encompasses acquiring a two-dimensional image including a depiction of an object, particularly of a tracking marker, employing artificial intelligence, particularly a machine-learning-model to extract information from the two-dimensional image so as to eventually determine the spatial position of the object with respect to the camera.

Description

[0001] MONOSCOPIC MARKER TRACKING

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a tracking system and computer-implemented method for determining the spatial position of a tracking marker, a corresponding computer program, a computer-readable storage medium storing such a program and a computer executing the program.

[0004] TECHNICAL BACKGROUND

[0005] Conventional optical tracking systems regularly utilize stereoscopic camera arrangements which include two optical cameras disposed relative to each other such that their respective fields of view overlap. With the spatial relative position of the cameras as well as the position of the depiction of an object in the respective coordinate systems of both cameras being known, the spatial position of the object with respect to the camera arrangement can be calculated, i.e. "triangulated".

[0006] In recent years, the use of augmented reality devices, particularly head-mounted augmented reality devices has become more and more popular in the medical field as these devices allow to supplement a practitioner's optical perception of the real world, for example a surgical site, with additional information, which is for example provided as graphical depiction, projected into the practitioner's field of view via a semi-transparent screen. Typical augmented reality devices include a monoscopic camera providing a video stream of the practitioner's field of view, on which basis the supplemented information can be positionally aligned with the practitioner's optical perception of real word objects. Embedding these augmented reality devices into the infrastructure of navigation systems however requires the coordinate system of the navigation system and the coordinate system of the augmented reality device to be spatially aligned, which is regularly done with the help of specific tracking references tracked over time by both, the augmented reality device and a tracking system assigned to the navigation system.

[0007] Providing specific tracking references however comes with additional effort in setting up the medical workplace, wherein additional care must be taken that the tracking reference is constantly recognized by both systems and that the respective lines of sight are not interrupted.

[0008] The present invention has the object of providing a straightforward approach of embedding an augmented reality device into the infrastructure of a navigation system, particularly a medical navigation system, which may even allow for utilizing the augmented reality device's monoscopic camera for tracking purposes so as to supplement or even replace conventional tracking systems such as stereoscopic optical tracking systems.

[0009] The present invention can be used for any tracking procedures, particularly in the medical field, which may involve the use of monoscopic tracking cameras for tracking purposes, thereby supplementing or even replacing conventional optical tracking systems such as Curve® or Kick®, both products of Brainlab AG.

[0010] Aspects of the present invention, examples and exemplary steps and their embodiments are disclosed in the following. Different exemplary features of the invention can be combined in accordance with the invention wherever technically expedient and feasible.

[0011] EXEMPLARY SHORT DESCRIPTION OF THE INVENTION

[0012] In the following, a short description of the specific features of the present invention is given which shall not be understood to limit the invention only to the features or a combination of the features described in this section. The invention encompasses acquiring a two-dimensional image including a depiction of an object, particularly of a tracking marker, employing artificial intelligence, particularly a machine-learning-model to extract information from the two-dimensional image so as to eventually determine the spatial position of the object with respect to the camera.

[0013] GENERAL DESCRIPTION OF THE INVENTION

[0014] In this section, a description of the general features of the present invention is given for example by referring to possible embodiments of the invention.

[0015] In general, the invention reaches the aforementioned object by providing, in a first aspect, a tracking system for determining the spatial position of a tracking marker, including a camera and a computer which is operatively coupled to the camera and has at least one processor adapted to carry out a method comprising the following steps:

[0016] - acquire image data describing an optical image received via the camera;

[0017] - determine, based on the image data, image section data describing an image section of the optical image, having a predefined pixel count in a horizontal and / or vertical direction of the image;

[0018] - determine, based on the image section data and by applying a machine-learning- model, feature detection data describing a detection of depictions of a plurality of features of the tracking marker within the image section, the features having a distinctive electromagnetic signature;

[0019] - determine, based on the feature detection data, image subsection data describing a plurality of image subsections of the image section, wherein each of the image subsections encloses a respective depiction of one of the plurality of features;

[0020] - determine, based on the image subsection data, image subsection position data describing the extent and position of the plurality of image subsections within the image plane of the image received via the camera;

[0021] - determine, based on the image data and the image subsection position data, feature subsection position data describing, for each of the plurality of image subsections, a spatial position of a depiction of a respective feature in the image subsection within the image received via the camera;

[0022] - determine, based on the image subsection position data and the feature subsection position data, image feature position data describing a spatial position of the depictions of the features within the image plane of the image received via the camera;

[0023] - acquire relative feature position data describing the spatial relative position of the plurality of features of the real-world tracking marker;

[0024] - determine, based on the image feature position data and the relative feature position data, marker position data describing the spatial position of the real-world tracking marker with respect to the camera.

[0025] The term "tracking marker" as used herein should be understood to encompass any conceivable type of tracking marker, for example a marker including a plurality of retro-reflective marker spheres in a predefined spatial arrangement, a marker being defined by a single, particularly integrally formed structure which includes a plurality of distinguishable features, or a flat (2D-)marker having its distinguishable features basically disposed in a two-dimensional plane.

[0026] The term "spatial position" as used herein may include the spatial location and / or the spatial orientation of an object. In particular, the spatial position may include three translational degrees of freedom and three rotational degrees of freedom in three- dimensional space. Specifically, the term "spatial position" may also be referred to as "6D-pose".

[0027] The expression "distinctive electromagnetic signature" as used herein may be understood as the specific optical appearance or the specific optical distinctiveness of a feature of the tracking marker in which the feature differs from other parts, particularly the remaining parts of the tracking marker.

[0028] In a (for example first) exemplary step, the image received via the camera may have already undergone any kind of pre-processing before being fed to the method described herein. For example, the at least one image or video frame of a video stream is, after being received by the camera's CCD- or CMOS-sensor and before the inventive method sets in, may be processed in any conceivable manner, for example to optimize the image data for further processing.

[0029] In a (for example second) exemplary step, the image received via the camera is transferred to a predefined format having a predefined number of pixels in a vertical direction and / or a horizontal direction of the image so as to facilitate further processing of image information. If the image received by the camera already features the predefined pixel count in a horizontal and / or a vertical direction of the image, this step can be omitted. In particular, the image section can represent the entire image received via the camera, or a subset of the image received via the camera.

[0030] In a (for example third) exemplary step, the image section having a predefined pixel count is processed, thereby utilizing a machine-learning-algorithm that has been trained to detect the presence of a plurality of optically distinguishable features of the tracking marker within the image section referred to above.

[0031] In this context, a machine-learning-algorithm can be trained to detect, i.e. to label a depiction of one or more features in the image section in association with one or more training images showing a depiction of this feature. This may involve the use of a convolutional network. Convolutional networks, also known as convolutional neural networks or CNNs, are an example of neural networks for processing data that has a known grid-like topology. Examples include time-series data, which can be thought of as a 1 -D grid taking samples at regular time intervals, and image data, which can be thought of as a 2-D grid of pixels. The name “convolutional neural network” indicates that the network employs the mathematical operation of convolution. Convolution is a linear operation. Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers. There are several variants on the convolution function that are widely used in practice for neural networks. In general, the operation used in a convolutional neural network does not correspond precisely to the definition of convolution as used in other fields, such as engineering or pure mathematics. In a (for example fourth) exemplary step, an image subsection, which may also be referred to as "bounding box", is defined for each detected depiction of a feature. In other words, bounding boxes are defined which represent those parts of the image section, which show a depiction of a marker feature. These bounding boxes may have any conceivable shape, for example a rectangular shape or square shape.

[0032] In a (for example fifth) exemplary step, the bounding boxes defined in the image section having a predefined pixel count are transferred back to the image received via the camera and having the original pixel count or resolution. In particular, the position and extent of the bounding boxes that have initially been defined within the image section is determined within the image having the original resolution. In particular, the bounding boxes calculated for the image having the original resolution cover the same image content as their respective counterparts in the image section having the predefined resolution.

[0033] In a (for example sixth) exemplary step, the bounding boxes determined in the preceding step, each encompass a depiction of a marker feature the position of which is determined within the respective bounding box defined for the image having the original resolution. As will be described further below, this may involve applying one of a plurality of available approaches to calculate the position of the depiction of the feature within a respective bounding box.

[0034] In a (for example seventh) exemplary step, the information about the position of the bounding boxes within the image having the original resolution, and the information about the position of the feature depictions within the respective bounding boxes allows for calculating the position of the feature depictions within the image having the original resolution. At this point, the position of the plurality of features within the camera's field of view and projected onto the 2D-image-plane is accurately known.

[0035] In a (for example eighth) exemplary step, the expected relative position of the plurality of marker features may be taken or acquired from a three-dimensional model of a real-world tracking marker which is seen on the image received via the camera, i.e. a depiction of which is shown on the image. In a (for example ninth) exemplary step, the information about the 2D-position of the features within the plane of the image received via the camera on the one hand, and the 3D-relative position of the features acquired from the model on the other hand eventually allows for determining the spatial position of the actual tracking marker with respect to the camera. This may involve the use of one of the applicable approaches described below.

[0036] In an example of the tracking system according to the first aspect, the camera

[0037] - is adapted to receive electromagnetic radiation in the visible spectrum of light;

[0038] - features a horizontal and / or a vertical pixel count of at least 3000 pixels;

[0039] - is either integrated in a housing configured to be statically positioned, particularly fixedly mounted within a room of a building; or

[0040] - is integrated in a wearable device, particularly in a head-mounted augmented reality device, specifically in AR-(augmented reality)glasses.

[0041] In particular, the camera may be an RGB-camera. It may also be a camera which is capable of generating an RGB-image, particularly via a first CCD- or CMOS-sensor, as well as of generating an IR-(infrared)-image, particularly via a second CCD- or CMOS-sensor. The camera may also be a high-resolution-camera, particularly a 4K- camera or higher. Further, the camera may form part of a head-mounted AR-device, particularly AR-glasses, which has a semi-transparent screen for having graphical information projected into the field of view of a person wearing the head-mounted AR-device. In the alternative, the camera may also be configured as "static" -camera, i.e. a camera which is embedded in a casing or housing meant to maintain its spatial position.

[0042] It should be noted that the computer processor of the above-described tracking system according to the first aspect may be adapted to carry out any method described in the following. In particular, the tracking system according to the first aspect may include a camera and a computer which is operatively coupled to the camera and has at least one processor adapted to carry out the method according to any one of the claims 3 to 12. According to a second aspect of the invention, a method comprises executing, on at least one processor of at least one computer (for example at least one computer being part of a navigation system), the following exemplary steps which are executed by the at least one processor:

[0043] - acquiring image data describing an optical image received via the camera;

[0044] - determining, based on the image data, image section data describing an image section of the optical image, having a predefined pixel count in a horizontal and / or vertical direction of the image;

[0045] - determining, based on the image section data and by applying a machine-learning- model, feature detection data describing a detection of depictions of a plurality of features of the tracking marker within the image section, the features having a distinctive electromagnetic signature;

[0046] - determining, based on the feature detection data, image subsection data describing a plurality of image subsections of the image section, wherein each of the image subsections encloses a respective depiction of one of the plurality of features;

[0047] - determining, based on the image subsection data, image subsection position data describing the extent and position of the plurality of image subsections within the image plane of the image received via the camera;

[0048] - determining, based on the image data and the image subsection position data, feature subsection position data describing, for each of the plurality of image subsections, a spatial position of a depiction of a respective feature in the image subsection within the image received via the camera;

[0049] - determining, based on the image subsection position data and the feature subsection feature position data, image feature position data describing a spatial position of the depictions of the features within the image plane of the image received via the camera;

[0050] - acquiring relative feature position data describing the spatial relative position of the plurality of features of the real-world tracking marker;

[0051] - determining, based on the image feature position data and the relative feature position data, marker position data describing the spatial position of the tracking marker with respect to the camera.

[0052] In an example of the method according to the second aspect, determining image section data involves: - a modification, particularly a reduction of the pixel count in a horizontal and / or in a vertical direction of the image received via the camera; and / or

[0053] - a selection of a subsection from the image received via the camera.

[0054] In other words, the image section may be obtained by "resizing" the image received via the camera. In a specific example, the image content of the image received via the camera remains the same in the image section, though with a reduced pixel count or resolution in a horizontal and / or vertical direction of the image.

[0055] Additionally or alternatively, the image section may also be obtained by "cropping" the image received via the camera. In other words, the image section represents a cut-out of the image received via the camera. In a specific example, the image section is obtained by both, "resizing" and "cropping" the image received via the camera.

[0056] In a further example of the method according to the second aspect, determining feature detection data involves applying a convolutional-neural-network for an object- detection-model, particularly YOLOv5 or higher, specifically YOLOv8, to detect depictions of a plurality of features of the tracking marker within the image section.

[0057] As was already described above, a convolutional-neural-network may be utilized for detecting depictions of the features of a tracking marker within the image section. Considering the use of YOLOv5, it becomes apparent that an image section having a predefined resolution needs to be obtained from a high resolution image as received via the camera. For example, YOLOv5 is adapted to process images having a pixel count of 640 x 640 pixels so as to reduce computational effort to a reasonable level.

[0058] In a further example of the method according to the second aspect, the machine- learning-model, particularly the object-detection-model, specifically YOLOv5 has been trained to detect depictions of the entire tracking marker and / or of the features of the tracking marker within the image section, particularly wherein the features have a predefined

[0059] - two-dimensional or three-dimensional geometric shape; and / or

[0060] - size; and / or - colour; specifically wherein all of said features are identical.

[0061] In particular, this may involve rejecting detected depictions of features lying remote from the detected depiction of the tracking marker, particularly lying outside a predefined boundary around the depiction of the tracking marker.

[0062] By doing so, "outlying" depictions are considered as "false positives" and it is assumed that these depictions do not show a feature of the tracking marker.

[0063] In this context, it should be noted that a specific embodiment of the inventive approach may gather information from a conventional tracking system so as to distinguish between two or more tracking markers disposed close to each other: In a case where two or more tracking markers, each having a plurality of marker features, are disposed close to each other, another tracking system may provide information on the three-dimensional position of these tracking markers and of respective instruments or devices so as to assign the detected features to a specific tracking marker. Once this is done, the approach disclosed herein is applied to each tracking marker separately to eventually determine its spatial position with respect to the camera.

[0064] In another example of the method according to the second aspect, determining image subsection data involves defining the dimensions of at least one of the image subsections to have the image subsection enclose the entire depiction of a respective feature, with the ratio of the entire image subsection to those parts of the image subsection which do not depict the respective feature being above a predefined threshold. In order to reduce computational effort, it may be desirable to define the "bounding boxes" to include as little as possible of image content which does not show a marker feature, but rather shows image "background".

[0065] The extent, i.e. the image content covered by the bounding boxes transferred back to the image received via the camera may however be increased as compared to the extent or coverage of the respective bounding boxes originally defined within the image section. This ensures that the feature depictions are safely contained within the respective bounding boxes in the image received via the camera, which in turn improves stability of the processing steps which are to follow. Therefore, in another example of the method according to the second aspect, determining image subsection position data involves increasing the horizontal and / or vertical extent of at least one image subsection within the image received via the camera, and / or wherein determining image subsection position data involves reducing the ratio of the entire image subsection to those parts of the image subsection which do not depict the respective feature.

[0066] In a further example of the method according to the second aspect, determining feature subsection position data involves applying

[0067] - a sphere-centering-algorithm;

[0068] - a machine-learning-model, particularly a convolutional-neural-network (CNN); and / or

[0069] - an edge-detection-algorithm, particularly a canny-edge-detection-algorithm, which is particularly preceded by a median-blur-algorithm.

[0070] For determining the position of the feature depictions within the respective bounding boxes in the image received via the camera, a plurality of approaches is available.

[0071] The term "sphere-centering-algorithm" refers to a computational technique known in the art, which is used to precisely identify the center and radius of spherical objects within two-dimensional images. This method may employ various strategies, for example iterative approaches, the utilization of a Hough-transformation for circle detection, or the incorporation of a convolutional neural network (CNN) in the form of an encoder. Its primary goal is to achieve accurate center and radius determination for spherical objects within two-dimensional images. One of these approaches is described by Toth and Hajder in "A Minimal Solution for Image-Based Sphere Estimation", International Journal of Computer Vision (2023) 131 :1428-1447. An alternative approach may involve the use of a machine-learning-model, which may be very similar to the machine-learning-model as described above. Another alternative may involve the use of a (canny-)edge-detection-algorithm which may be preceded by a (median-)blur-algorithm, both of which are well-known approaches in the art. In a further example of the method according to the second aspect, acquiring image data involves:

[0072] - acquiring a plurality of optical images received via the camera, particularly a video stream including a plurality of optical image frames; and / or

[0073] - acquiring, for each image or frame acquired via the camera, camera intrinsic parameter data describing at least one intrinsic parameter and / or at least one camera calibration parameter assigned to a respective image or frame.

[0074] In the alternative, the inventive approach may also involve the use of a so-called calibrated camera for obtaining the optical image. These approaches allow for a correction of possible image-distortions and to calculate the accurate spatial position based on these images in the first place.

[0075] In a further example of the method according to the second aspect, determining marker position data involves applying a perspective-n-point (PnP)-algorithm. Such PnP-algorithm is an approach well known in the art for extracting a three-dimensional position (including three translational and three rotational degrees of freedom) of an object from a two-dimensional image of that object.

[0076] In a third aspect, the invention is directed to a computer program comprising instructions which, when the program is executed by at least one computer, causes the at least one computer to carry out method according to the first aspect. The invention may alternatively or additionally relate to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, such as an electromagnetic carrier wave carrying information which represents the program, for example the aforementioned program, which for example comprises code means which are adapted to perform any or all of the steps of the method according to the first aspect. The signal wave is in one example a data carrier signal carrying the aforementioned computer program. A computer program stored on a disc is a data file, and when the file is read out and transmitted it becomes a data stream for example in the form of a (physical, for example electrical, for example technically generated) signal. The signal can be implemented as the signal wave, for example as the electromagnetic carrier wave which is described herein. For example, the signal, for example the signal wave is constituted to be transmitted via a computer network, for example LAN, WLAN, WAN, mobile network, for example the internet. For example, the signal, for example the signal wave, is constituted to be transmitted by optic or acoustic data transmission. The invention according to the second aspect therefore may alternatively or additionally relate to a data stream representative of the aforementioned program, i.e. comprising the program.

[0077] In a fourth aspect, the invention is directed to a computer-readable storage medium on which the program according to the second aspect is stored. The program storage medium is for example non-transitory.

[0078] In a fifth aspect, the invention is directed to at least one computer (for example, a computer), comprising at least one processor (for example, a processor), wherein the program according to the second aspect is executed by the processor, or wherein the at least one computer comprises the computer-readable storage medium according to the third aspect.

[0079] Alternatively or additionally, the invention according to the fifth aspect is directed to a for example non-transitory computer-readable program storage medium storing a program for causing the computer according to the fourth aspect to execute the data processing steps of the method according to the first aspect.

[0080] For example, the disclosed method is not a method for treatment of the human or animal body by surgery or therapy. For example, the invention does not involve or in particular comprise or encompass an invasive step which would represent a substantial physical interference with the body requiring professional medical expertise to be carried out and entailing a substantial health risk even when carried out with the required professional care and expertise.

[0081] The invention does not involve or in particular comprise or encompass any surgical or therapeutic activity. The invention is instead directed as applicable to tracking of objects over time. For this reason alone, no surgical or therapeutic activity and in particular no surgical or therapeutic step is necessitated or implied by carrying out the invention. Use of the device or system

[0082] The present invention also relates to the use of the device / system or any embodiment thereof for tracking objects over time.

[0083] DEFINITIONS

[0084] In this section, definitions for specific terminology used in this disclosure are offered which also form part of the present disclosure.

[0085] Computer-implemented method

[0086] The method in accordance with the invention is for example a computer-implemented method. For example, all the steps or merely some of the steps (i.e. less than the total number of steps) of the method in accordance with the invention can be executed by a computer (for example, at least one computer). An embodiment of the computer implemented method is a use of the computer for performing a data processing method. An embodiment of the computer implemented method is a method concerning the operation of the computer such that the computer is operated to perform one, more or all steps of the method.

[0087] The computer for example comprises at least one processor and for example at least one memory in order to (technically) process the data, for example electronically and / or optically. The processor being for example made of a substance or composition which is a semiconductor, for example at least partly n- and / or p-doped semiconductor, for example at least one of II-, III-, IV-, V-, Vl-sem iconductor material, for example (doped) silicon and / or gallium arsenide. The calculating or determining steps described are for example performed by a computer. Determining steps or calculating steps are for example steps of determining data within the framework of the technical method, for example within the framework of a program. A computer is for example any kind of data processing device, for example electronic data processing device. A computer can be a device which is generally thought of as such, for example desktop PCs, notebooks, netbooks, etc., but can also be any programmable apparatus, such as for example a mobile phone or an embedded processor. A computer can for example comprise a system (network) of "subcomputers", wherein each sub-computer represents a computer in its own right. The term "computer" includes a cloud computer, for example a cloud server. The term computer includes a server resource. The term "cloud computer" includes a cloud computer system which for example comprises a system of at least one cloud computer and for example a plurality of operatively interconnected cloud computers such as a server farm. Such a cloud computer is preferably connected to a wide area network such as the world wide web (WWW) and located in a so-called cloud of computers which are all connected to the world wide web. Such an infrastructure is used for "cloud computing", which describes computation, software, data access and storage services which do not require the end user to know the physical location and / or configuration of the computer delivering a specific service. For example, the term "cloud" is used in this respect as a metaphor for the Internet (world wide web). For example, the cloud provides computing infrastructure as a service (laaS). The cloud computer can function as a virtual host for an operating system and / or data processing application which is used to execute the method of the invention. The cloud computer is for example an elastic compute cloud (EC2) as provided by Amazon Web Services™. A computer for example comprises interfaces in order to receive or output data and / or perform an analogue-to-digital conversion. The data are for example data which represent physical properties and / or which are generated from technical signals. The technical signals are for example generated by means of (technical) detection devices (such as for example devices for detecting marker devices) and / or (technical) analytical devices (such as for example devices for performing (medical) imaging methods), wherein the technical signals are for example electrical or optical signals. The technical signals for example represent the data received or outputted by the computer. The computer is preferably operatively coupled to a display device which allows information outputted by the computer to be displayed, for example to a user. One example of a display device is a virtual reality device or an augmented reality device (also referred to as virtual reality glasses or augmented reality glasses) which can be used as "goggles" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device or a virtual reality device can be used both to input information into the computer by user interaction and to display information outputted by the computer. Another example of a display device would be a standard computer monitor comprising for example a liquid crystal display operatively coupled to the computer for receiving display control data from the computer for generating signals used to display image information content on the display device. A specific embodiment of such a computer monitor is a digital lightbox. An example of such a digital lightbox is Buzz®, a product of Brainlab AG. The monitor may also be the monitor of a portable, for example handheld, device such as a smart phone or personal digital assistant or digital media player.

[0088] The invention also relates to a computer program comprising instructions which, when on the program is executed by a computer, cause the computer to carry out the method or methods, for example, the steps of the method or methods, described herein and / or to a computer-readable storage medium (for example, a non-transitory computer-readable storage medium) on which the program is stored and / or to a computer comprising said program storage medium and / or to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, such as an electromagnetic carrier wave carrying information which represents the program, for example the aforementioned program, which for example comprises code means which are adapted to perform any or all of the method steps described herein. The signal wave is in one example a data carrier signal carrying the aforementioned computer program. The invention also relates to a computer comprising at least one processor and / or the aforementioned computer- readable storage medium and for example a memory, wherein the program is executed by the processor.

[0089] Within the framework of the invention, computer program elements can be embodied by hardware and / or software (this includes firmware, resident software, micro-code, etc.). Within the framework of the invention, computer program elements can take the form of a computer program product which can be embodied by a computer-usable, for example computer-readable data storage medium comprising computer-usable, for example computer-readable program instructions, "code" or a "computer program" embodied in said data storage medium for use on or in connection with the instruction-executing system. Such a system can be a computer; a computer can be a data processing device comprising means for executing the computer program elements and / or the program in accordance with the invention, for example a data processing device comprising a digital processor (central processing unit or CPU) which executes the computer program elements, and optionally a volatile memory (for example a random access memory or RAM) for storing data used for and / or produced by executing the computer program elements. Within the framework of the present invention, a computer-usable, for example computer-readable data storage medium can be any data storage medium which can include, store, communicate, propagate or transport the program for use on or in connection with the instructionexecuting system, apparatus or device. The computer-usable, for example computer- readable data storage medium can for example be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device or a medium of propagation such as for example the Internet. The computer- usable or computer-readable data storage medium could even for example be paper or another suitable medium onto which the program is printed, since the program could be electronically captured, for example by optically scanning the paper or other suitable medium, and then compiled, interpreted or otherwise processed in a suitable manner. The data storage medium is preferably a non-volatile data storage medium. The computer program product and any software and / or hardware described here form the various means for performing the functions of the invention in the example embodiments. The computer and / or data processing device can for example include a guidance information device which includes means for outputting guidance information. The guidance information can be outputted, for example to a user, visually by a visual indicating means (for example, a monitor and / or a lamp) and / or acoustically by an acoustic indicating means (for example, a loudspeaker and / or a digital speech output device) and / or tactilely by a tactile indicating means (for example, a vibrating element or a vibration element incorporated into an instrument). For the purpose of this document, a computer is a technical computer which for example comprises technical, for example tangible components, for example mechanical and / or electronic components. Any device mentioned as such in this document is a technical and for example tangible device. Acquiring data

[0090] The expression "acquiring data" for example encompasses (within the framework of a computer implemented method) the scenario in which the data are determined by the computer implemented method or program. Determining data for example encompasses measuring physical quantities and transforming the measured values into data, for example digital data, and / or computing (and e.g. outputting) the data by means of a computer and for example within the framework of the method in accordance with the invention. A step of “determining” as described herein for example comprises or consists of issuing a command to perform the determination described herein. For example, the step comprises or consists of issuing a command to cause a computer, for example a remote computer, for example a remote server, for example in the cloud, to perform the determination. Alternatively or additionally, a step of “determination” as described herein for example comprises or consists of receiving the data resulting from the determination described herein, for example receiving the resulting data from the remote computer, for example from that remote computer which has been caused to perform the determination. The meaning of "acquiring data" also for example encompasses the scenario in which the data are received or retrieved by (e.g. input to) the computer implemented method or program, for example from another program, a previous method step or a data storage medium, for example for further processing by the computer implemented method or program. Generation of the data to be acquired may but need not be part of the method in accordance with the invention. The expression "acquiring data" can therefore also for example mean waiting to receive data and / or receiving the data. The received data can for example be inputted via an interface. The expression "acquiring data" can also mean that the computer implemented method or program performs steps in order to (actively) receive or retrieve the data from a data source, for instance a data storage medium (such as for example a ROM, RAM, database, hard drive, etc.), or via the interface (for instance, from another computer or a network). The data acquired by the disclosed method or device, respectively, may be acquired from a database located in a data storage device which is operably to a computer for data transfer between the database and the computer, for example from the database to the computer. The computer acquires the data for use as an input for steps of determining data. The determined data can be output again to the same or another database to be stored for later use. The database or database used for implementing the disclosed method can be located on network data storage device or a network server (for example, a cloud data storage device or a cloud server) or a local data storage device (such as a mass storage device operably connected to at least one computer executing the disclosed method). The data can be made "ready for use" by performing an additional step before the acquiring step. In accordance with this additional step, the data are generated in order to be acquired. The data are for example detected or captured (for example by an analytical device). Alternatively or additionally, the data are inputted in accordance with the additional step, for instance via interfaces. The data generated can for example be inputted (for instance into the computer). In accordance with the additional step (which precedes the acquiring step), the data can also be provided by performing the additional step of storing the data in a data storage medium (such as for example a ROM, RAM, CD and / or hard drive), such that they are ready for use within the framework of the method or program in accordance with the invention. The step of "acquiring data" can therefore also involve commanding a device to obtain and / or provide the data to be acquired. In particular, the acquiring step does not involve an invasive step which would represent a substantial physical interference with the body, requiring professional medical expertise to be carried out and entailing a substantial health risk even when carried out with the required professional care and expertise. In particular, the step of acquiring data, for example determining data, does not involve a surgical step and in particular does not involve a step of treating a human or animal body using surgery or therapy. In order to distinguish the different data used by the present method, the data are denoted (i.e. referred to) as "XY data" and the like and are defined in terms of the information which they describe, which is then preferably referred to as "XY information" and the like.

[0091] Registering

[0092] The n-dimensional image of a body is registered when the spatial location of each point of an actual object within a space, for example a body part in an operating theatre, is assigned an image data point of an image (CT, MR, etc.) stored in a navigation system. Image registration

[0093] Image registration is the process of transforming different sets of data into one coordinate system. The data can be multiple photographs and / or data from different sensors, different times or different viewpoints. It is used in computer vision, medical imaging and in compiling and analysing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

[0094] Marker

[0095] It is the function of a marker to be detected by a marker detection device (for example, a camera or an ultrasound receiver or analytical devices such as CT or MRI devices) in such a way that its spatial position (i.e. its spatial location and / or alignment) can be ascertained. The detection device is for example part of a navigation system. The markers can be active markers. An active marker can for example emit electromagnetic radiation and / or waves which can be in the infrared, visible and / or ultraviolet spectral range. A marker can also however be passive, i.e. can for example reflect electromagnetic radiation in the infrared, visible and / or ultraviolet spectral range or can block X-ray radiation. To this end, the marker can be provided with a surface which has corresponding reflective properties or can be made of metal in order to block the X-ray radiation. It is also possible for a marker to reflect and / or emit electromagnetic radiation and / or waves in the radio frequency range or at ultrasound wavelengths. A marker preferably has a spherical and / or spheroid shape and can therefore be referred to as a marker sphere; markers can however also exhibit a cornered, for example cubic, shape.

[0096] Marker device

[0097] A marker device can for example be a reference star or a pointer or a single marker or a plurality of (individual) markers which are then preferably in a predetermined spatial relationship. A marker device comprises one, two, three or more markers, wherein two or more such markers are in a predetermined spatial relationship. This predetermined spatial relationship is for example known to a navigation system and is for example stored in a computer of the navigation system.

[0098] In another embodiment, a marker device comprises an optical pattern, for example on a two-dimensional surface. The optical pattern might comprise a plurality of geometric shapes like circles, rectangles and / or triangles. The optical pattern can be identified in an image captured by a camera, and the position of the marker device relative to the camera can be determined from the size of the pattern in the image, the orientation of the pattern in the image and the distortion of the pattern in the image. This allows determining the relative position in up to three rotational dimensions and up to three translational dimensions from a single two-dimensional image.

[0099] The position of a marker device can be ascertained, for example by a medical navigation system. If the marker device is attached to an object, such as a bone or a medical instrument, the position of the object can be determined from the position of the marker device and the relative position between the marker device and the object. Determining this relative position is also referred to as registering the marker device and the object. The marker device or the object can be tracked, which means that the position of the marker device or the object is ascertained twice or more over time.

[0100] Marker holder

[0101] A marker holder is understood to mean an attaching device for an individual marker which serves to attach the marker to an instrument, a part of the body and / or a holding element of a reference star, wherein it can be attached such that it is stationary and advantageously such that it can be detached. A marker holder can for example be rod-shaped and / or cylindrical. A fastening device (such as for instance a latching mechanism) for the marker device can be provided at the end of the marker holder facing the marker and assists in placing the marker device on the marker holder in a force fit and / or positive fit. Pointer

[0102] A pointer is a rod which comprises one or more - advantageously, two - markers fastened to it and which can be used to measure off individual co-ordinates, for example spatial co-ordinates (i.e. three-dimensional co-ordinates), on a part of the body, wherein a user guides the pointer (for example, a part of the pointer which has a defined and advantageously fixed position with respect to the at least one marker attached to the pointer) to the position corresponding to the co-ordinates, such that the position of the pointer can be determined by using a surgical navigation system to detect the marker on the pointer. The relative location between the markers of the pointer and the part of the pointer used to measure off co-ordinates (for example, the tip of the pointer) is for example known. The surgical navigation system then enables the location (of the three-dimensional co-ordinates) to be assigned to a predetermined body structure, wherein the assignment can be made automatically or by user intervention.

[0103] Reference star

[0104] A "reference star" refers to a device with a number of markers, advantageously three markers, attached to it, wherein the markers are (for example detachably) attached to the reference star such that they are stationary, thus providing a known (and advantageously fixed) position of the markers relative to each other. The position of the markers relative to each other can be individually different for each reference star used within the framework of a surgical navigation method, in order to enable a surgical navigation system to identify the corresponding reference star on the basis of the position of its markers relative to each other. It is therefore also then possible for the objects (for example, instruments and / or parts of a body) to which the reference star is attached to be identified and / or differentiated accordingly. In a surgical navigation method, the reference star serves to attach a plurality of markers to an object (for example, a bone or a medical instrument) in order to be able to detect the position of the object (i.e. its spatial location and / or alignment). Such a reference star for example features a way of being attached to the object (for example, a clamp and / or a thread) and / or a holding element which ensures a distance between the markers and the object (for example in order to assist the visibility of the markers to a marker detection device) and / or marker holders which are mechanically connected to the holding element and which the markers can be attached to.

[0105] Navigation system

[0106] The present invention is also directed to a navigation system for computer-assisted surgery. This navigation system preferably comprises the aforementioned computer for processing the data provided in accordance with the computer implemented method as described in any one of the embodiments described herein. The navigation system preferably comprises a detection device for detecting the position of detection points which represent the main points and auxiliary points, in order to generate detection signals and to supply the generated detection signals to the computer, such that the computer can determine the absolute main point data and absolute auxiliary point data on the basis of the detection signals received. A detection point is for example a point on the surface of the anatomical structure which is detected, for example by a pointer. In this way, the absolute point data can be provided to the computer. The navigation system also preferably comprises a user interface for receiving the calculation results from the computer (for example, the position of the main plane, the position of the auxiliary plane and / or the position of the standard plane). The user interface provides the received data to the user as information. Examples of a user interface include a display device such as a monitor, or a loudspeaker. The user interface can use any kind of indication signal (for example a visual signal, an audio signal and / or a vibration signal). One example of a display device is an augmented reality device (also referred to as augmented reality glasses) which can be used as so-called "goggles" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device can be used both to input information into the computer of the navigation system by user interaction and to display information outputted by the computer.

[0107] The invention also relates to a navigation system for computer-assisted surgery, comprising: a computer for processing the absolute point data and the relative point data; a detection device for detecting the position of the main and auxiliary points in order to generate the absolute point data and to supply the absolute point data to the computer; a data interface for receiving the relative point data and for supplying the relative point data to the computer; and a user interface for receiving data from the computer in order to provide information to the user, wherein the received data are generated by the computer on the basis of the results of the processing performed by the computer.

[0108] Surgical navigation system

[0109] A navigation system, such as a surgical navigation system, is understood to mean a system which can comprise: at least one marker device; a transmitter which emits electromagnetic waves and / or radiation and / or ultrasound waves; a receiver which receives electromagnetic waves and / or radiation and / or ultrasound waves; and an electronic data processing device which is connected to the receiver and / or the transmitter, wherein the data processing device (for example, a computer) for example comprises a processor (CPU) and a working memory and advantageously an indicating device for issuing an indication signal (for example, a visual indicating device such as a monitor and / or an audio indicating device such as a loudspeaker and / or a tactile indicating device such as a vibrator) and a permanent data memory, wherein the data processing device processes navigation data forwarded to it by the receiver and can advantageously output guidance information to a user via the indicating device. The navigation data can be stored in the permanent data memory and for example compared with data stored in said memory beforehand.

[0110] Imaging geometry

[0111] The information on the imaging geometry preferably comprises information which allows the analysis image (X-ray image) to be calculated, given a known relative position between the imaging geometry analysis apparatus and the analysis object (anatomical body part) to be analysed by X-ray radiation, if the analysis object which is to be analysed is known, wherein "known" means that the spatial geometry (size and shape) of the analysis object is known. This means for example that three- dimensional, "spatially resolved" information concerning the interaction between the analysis object (anatomical body part) and the analysis radiation (X-ray radiation) is known, wherein "interaction" means for example that the analysis radiation is blocked or partially or completely allowed to pass by the analysis object. The location and in particular orientation of the imaging geometry is for example defined by the position of the X-ray device, for example by the position of the X-ray source and the X-ray detector and / or for example by the position of the multiplicity (manifold) of X-ray beams which pass through the analysis object and are detected by the X-ray detector. The imaging geometry for example describes the position (i.e. the location and in particular the orientation) and the shape (for example, a conical shape exhibiting a specific angle of inclination) of said multiplicity (manifold). The position can for example be represented by the position of an X-ray beam which passes through the centre of said multiplicity or by the position of a geometric object (such as a truncated cone) which represents the multiplicity (manifold) of X-ray beams. Information concerning the above-mentioned interaction is preferably known in three dimensions, for example from a three-dimensional CT, and describes the interaction in a spatially resolved way for points and / or regions of the analysis object, for example for all of the points and / or regions of the analysis object. Knowledge of the imaging geometry for example allows the location of a source of the radiation (for example, an X-ray source) to be calculated relative to an image plane (for example, the plane of an X-ray detector). With respect to the connection between three- dimensional analysis objects and two-dimensional analysis images as defined by the imaging geometry, reference is made for example to the following publications:

[0112] 1. "An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision", Roger Y. Tsai, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami Beach, Florida, 1986, pages 364-374

[0113] 2. "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses", Roger Y. Tsai, IEEE Journal of Robotics and Automation, Volume RA-3, No. 4, August 1987, pages 323-344. 3. Yaniv Z., Joskowicz L., Simkin A., Garza-Jinich M., Milgrom C. (1998) Fluoroscopic image processing for computer-aided orthopaedic surgery. In: Wells W.M., Colchester A., Delp S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg

[0114] 4. EP 08 156293.6

[0115] 5. US 61 / 054,187

[0116] BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In the following, the invention is described with reference to the appended figures which give background explanations and represent specific embodiments of the invention. The scope of the invention is however not limited to the specific features disclosed in the context of the figures, wherein

[0118] Fig. 1 illustrates the basic steps of the method according to the second aspect;

[0119] Fig. 2 shows a specific embodiment of the method according to the second aspect;

[0120] Fig. 3 shows a specific embodiment of the method according to the second aspect;

[0121] Fig. 4 is a schematic illustration of the system according to the first aspect;

[0122] Fig. 5 is an illustration of the system according to the first aspect.

[0123] DESCRIPTION OF EMBODIMENTS

[0124] Fig. 1 illustrates the basic steps of the method according to the first aspect, in which step S11 encompasses acquiring image data, step S12 encompasses determining image section data, step S13 encompasses determining feature detection data, step S14 encompasses determining image subsection data, step S15 encompasses determining image subsection position data, step S16 encompasses determining feature subsection position data, step S17 encompasses determining image feature position data, step S18 encompasses acquiring relative feature position data, and step S19 encompasses determining marker position data.

[0125] While Figure 1 shows all of the necessary steps of the inventive approach, Figure 2 provides a more illustrative visualization of the inventive approach: In step S11 , a high-resolution RGB-image is acquired via an optical camera 4 which is for example integrated in a head-mounted augmented reality device 7 (cf. Figure 5). Since the pixel count of this image is not suitable for further processing as will be described further below, the image resolution needs to be reduced without loosing image content, i.e. the depiction of the tracking marker 8 having four features 9 (cf. Figure 5). Therefore, in step S12 the camera image is first “resized” to a pixel count of 1280 x 960, wherein an image section having a pixel count 640 x 640 is “cropped” from the resized image.

[0126] In the subsequent step S13, YOLOv5 is applied so as to detect depictions of features 9 of tracking marker 8 within the image section. Based on the machine-learning- model of YOLOv5, the position of features 9 within the image section is then detected and bounding boxes are defined in step S14 for each feature 9. The bounding boxes snugly fit around the respective features 9.

[0127] The position and extent of each of these bounding boxes is subsequently calculated within the original high-resolution image retrieved from the camera 7. Even though it can be assumed that the bounding boxes calculated for the original image fully enclose a respective depiction of a feature 9, the bounding boxes’ boundaries are enlarged by a predefined amount so as to ensure that the bounding boxes contain the depiction of the features 9 even when inaccuracies in image processing would mean that the actual position of the feature depictions deviates from the expected positions of the feature depictions. If the bounding boxes were defined in the original image to also snugly fit around respective feature depictions, these depictions may, at least to some extent, lie beyond the boundaries of the bounding boxes, such that further processing based on the image content of the affected bounding boxes would be impaired. In a subsequent step S16, the position of the bounding boxes is calculated within the image plane of the original image. Further, in subsequent step S17, the position of the feature depictions (i.e. the center of the depiction of the spherical features 9 in the shown example) is determined within the respective bounding boxes so as to eventually determine the position of each of the feature depictions within the original image.

[0128] By applying a Perspective-n-Point-algorithm in step S19, the spatial position (i.e. the spatial location and the spatial orientation) of the tracking marker 8 is calculated from the known position of the feature depictions in the original image acquired via the camera 4 and the expected spatial position of the features 9 with respect to each other, which is known from a model of a real-world tracking marker 8 stored on the storage device 6 (cf. Figure 4).

[0129] Fig. 3 illustrates an embodiment of the present invention that includes all essential features of the invention. In this embodiment, the entire data processing which is part of the method according to the second aspect is performed by a computer 2. Reference sign 1 denotes the input of data acquired by the method according to the second aspect into the computer 2 and reference sign 3 denotes the output of data determined by the method according to the second aspect.

[0130] Fig. 4 is a schematic illustration of the tracking system 4 according to the first aspect. The system is in its entirety identified by reference sign 4 and comprises a computer 5, an electronic data storage device (such as a hard disc) 6 for storing at least the relative feature position data and a RGB-camera 7. The components of the tracking system 4 have the functionalities and properties explained above with regard to the first aspect of this disclosure.

[0131] Fig. 5 shows a specific example of the tracking system according to the first aspect. A conventional optical tracking system 10 having a first coordinate system 11 is spatially disposed such that the field of view of its stereoscopic camera array 12 covers the spatial volume within which the tracking marker 8 along with its features 9 is present. In addition to the conventional tracking system 10, a head-mounted augmented reality device 7 having a forward facing camera 4 and having a second coordinate system 12 is also provided.

[0132] The inventive approach not only allows for aligning the first coordinate system 11 and the second coordinate system 12 based on detecting and tracking the spatial position of the tracking marker 8 with the conventional tracking system 10 and the headmounted augmented reality device 7, but even allows for replacing the conventional tracking system 10 with the head-mounted augmented reality device 7 for tracking the spatial position of the tracking member 8 and other trackable devices 13.

Claims

CLAIMS1. A tracking system for determining the spatial position of a tracking marker, including a camera and a computer which is operatively coupled to the camera and has at least one processor adapted to carry out a method comprising the following steps:- acquire image data describing an optical image received via the camera;- determine, based on the image data, image section data describing an image section of the optical image, having a predefined pixel count in a horizontal and / or vertical direction of the image;- determine, based on the image section data and by applying a machine-learning- model, feature detection data describing a detection of depictions of a plurality of features of the tracking marker within the image section, the features having a distinctive electromagnetic signature;- determine, based on the feature detection data, image subsection data describing a plurality of image subsections of the image section, wherein each of the image subsections encloses a respective depiction of one of the plurality of features;- determine, based on the image subsection data, image subsection position data describing the extent and position of the plurality of image subsections within the image plane of the image received via the camera;- determine, based on the image data and the image subsection position data, feature subsection position data describing, for the plurality of image subsections, particularly each one of the plurality of image subsections, a spatial position of a depiction of a respective feature in the image subsection within the image received via the camera;- determine, based on the image subsection position data and the feature subsection position data, image feature position data describing a spatial position of the depictions of the features within the image plane of the image received via the camera;- acquire relative feature position data describing the spatial relative position of the plurality of features of the real-world tracking marker;- determine, based on the image feature position data and the relative feature position data, marker position data describing the spatial position of the tracking marker with respect to the camera.

2. The tracking system according to claim 1 , wherein the camera- is adapted to receive electromagnetic radiation in the visible spectrum of light;- features a horizontal and / or a vertical resolution of at least 3000 pixels;- is either integrated in a housing configured to be statically positioned, particularly fixedly mounted within a room of a building; or- is integrated in a wearable device, particularly in a head-mounted augmented reality device, specifically in AR-(augmented reality)glasses.

3. A computer-implemented method of determining the spatial position of a tracking marker, the method comprising the following steps:- acquiring image data (S11 ) describing an optical image received via the camera;- determining, based on the image data, image section data (S12) describing an image section of the optical image, having a predefined pixel count in a horizontal and / or vertical direction of the image;- determining, based on the image section data and by applying a machine-learning- model, feature detection data (S13) describing a detection of depictions of a plurality of features of the tracking marker within the image section, the features having a distinctive electromagnetic signature;- determining, based on the feature detection data, image subsection data (S14) describing a plurality of image subsections of the image section, wherein each of the image subsections encloses a respective depiction of one of the plurality of features;- determining, based on the image subsection data, image subsection position data (S15) describing the extent and position of the plurality of image subsections within the image plane of the image received via the camera;- determining, based on the image data and the image subsection position data, feature subsection position data (S16) describing, for each of the plurality of image subsections, a spatial position of a depiction of a respective feature in the image subsection within the image received via the camera;- determining, based on the image subsection position data and the feature subsection position data, image feature position data (S17) describing a spatialposition of the depictions of the features within the image plane of the image received via the camera;- acquiring relative feature position data (S18) describing the spatial relative position of the plurality of features of the real-world tracking marker;- determining, based on the image feature position data and the relative feature position data, marker position data (S19) describing the spatial position of the real- world tracking marker with respect to the camera.

4. The method of claim 3, wherein determining image section data involves- a modification, particularly a reduction of the pixel count in a horizontal and / or in a vertical direction of the image received via the camera; and / or- a selection of a subsection from the image received via the camera;5. The method of any one of claims 3 and 4, wherein determining feature detection data involves applying a convolutional-neural-network for an object-detection-model, particularly YOLOv5 to detect depictions of a plurality of features of the tracking marker within the image section.

6. The method of any one of claims 3 to 5, wherein the machine-learning-model, particularly the object-detection-model, specifically YOLOv5 has been trained to detect depictions of the tracking marker and / or of the features of the tracking marker within the image section, particularly wherein the features have a predefined- two-dimensional or three-dimensional geometric shape; and / or- size; and / or- colour; specifically wherein all of said features are identical.

7. The method of claim 6, wherein determining feature detection data involves rejecting detected depictions of features lying remote from the detected depiction of the tracking marker, particularly lying outside a predefined boundary around the depiction of the tracking marker.

8. The method of any one of claims 3 to 7, wherein determining image subsection data involves defining the dimensions of at least one of the image subsections to have the image subsection enclose the entire depiction of a respective feature, with the ratio of the entire image subsection to those parts of the image subsection which do not depict the respective feature being above a predefined threshold.

9. The method of any one of claims 3 to 8, wherein determining image subsection position data involves increasing the horizontal and / or vertical extent of at least one image subsection within the image received via the camera, and / or wherein determining image subsection position data involves reducing the ratio of the entire image subsection to those parts of the image subsection which do not depict the respective feature.

10. The method of any one of claims 3 to 9, wherein determining feature subsection position data involves applying- a sphere-centering-algorithm;- a machine-learning-model, particularly a convolutional-neural-network (CNN); and / or- an edge-detection-algorithm, particularly a canny-edge-detection-algorithm, which is particularly preceded by a median-blur-algorithm.11 . The method of any one of claims 3 to 10, wherein acquiring image data involves- acquiring a plurality of optical images received via the camera, particularly a video stream including a plurality of optical image frames; and / or- acquiring, for each image or frame acquired via the camera, camera intrinsic parameter data describing at least one intrinsic parameter and / or at least one camera calibration parameter assigned to a respective image or frame.

12. The method of any one of claims 3 to 11 , wherein determining marker position data involves applying a Perspective-n-Point(PnP)-algorithm.

13. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of the claims 3 to 12;and / or a computer-readable storage medium on which the program is stored; and / or a computer comprising at least one processor and / or the program storage medium, wherein the program is executed by the processor; and / or a data carrier signal carrying the program; and / or a data stream comprising the program.

14. Use of the system according to one of the claims 1 and 2 for conducting a medical procedure, wherein the use comprises execution of the steps of the method according to any one of the preceding method claims for determining the spatial position of a tracking marker.

Images