HK1251429A1 - Method and node for manufacturing a surgical kit for cartilage repair - Google Patents

Description

Method and node for manufacturing surgical equipment for cartilage repair

The present application is a divisional application of a patent application having an application date of 2014, 2/7, application number of 201480074864.6, entitled "method and node for manufacturing surgical equipment for cartilage repair".

Technical Field

Embodiments of the present invention generally relate to designing and manufacturing custom implants for use as patient management in healthcare based radiological imaging.

More particularly, various embodiments of the present invention relate to methods and systems for manufacturing surgical equipment for cartilage repair on articulating surfaces of a joint.

Background

Radiology is a medical technique that uses imaging to diagnose and treat problems associated with patient health. Radiology uses arrays in imaging techniques such as X-ray radiography, ultrasound, Computed Tomography (CT), nuclear medicine, Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI).

The patient is exposed to radiographic imaging techniques that provide information about the internal structure of the body by capturing three-dimensional radiographic images of, for example, the knee joint. This occurs in a medical facility such as a radiology department of a hospital. The obtained effects and information are forwarded to an implant design center for designing the implant and generating control software (CAD/CAM), e.g. for improving or repairing damaged cartilage, e.g. a damaged human knee.

In conventional systems, the implant is manufactured as a standard sized surgical equipment or may be provided with standard guides to support during the implantation procedure, such as supporting the determined position and the installation angle of the implant.

A problem with conventional systems is that the implant is difficult to customize to the patient, which can result in unnecessarily large area replacement on the intact cartilage, the top surface of the implant not being aligned with the top surface of the cartilage being replaced, which in turn can result in reduced or no improvement in the condition of the person undergoing the implantation procedure.

Another problem is that three-dimensional representations of internal surfaces of the human body, such as 3D surfaces obtained from radiological 3D images of the articulating surfaces of the joints, such three-dimensional representations generated based on three-dimensional radiological images are generally not as smooth as the surface of healthy cartilage tissue.

Yet another problem is to generate or acquire an accurate three-dimensional representation of the joint surface based on the 3D radiological image and a segmentation process, wherein the segmentation process is controlled by a set of segmentation process control parameters.

Another problem is that by obtaining a set of segmentation process control parameters that may enable improved manufacturing of patient-customized surgical equipment for cartilage repair, an intact cartilage top surface is estimated in areas with an intact cartilage top surface.

Accordingly, there is a need for a system and method that can improve the manufacture of surgical equipment for cartilage repair.

Disclosure of Invention

It is an object of the present invention to improve the manufacture of patient-customized surgical equipment for cartilage repair.

Summary of the invention

Embodiments of the present invention relate to improved manufacture of surgical equipment for cartilage repair. Some embodiments include receiving radiographic image data representing a three-dimensional image of a joint, generating a first three-dimensional representation of a first surface of the joint during scalable image segmentation, dependent on an updated set of segmentation process control parameters and the radiographic image data, generating a second three-dimensional representation of a second surface of the joint during scalable dynamic modeling, dependent on an updated set of dynamic modeling process control parameters and the radiographic image data, generating a cartilage lesion perimeter CDP based on the radiographic image data, generating a set of data representing a geometric object based on the first surface, the second surface, and the CDP, wherein the geometric objects represent the determined cartilage damage and CDP, and generate control software for controlling the CAD or CAM system, to manufacture a surgical equipment for cartilage repair according to the set of data representing the geometric object and a predetermined model of the components of said surgical equipment.

Another benefit of the present invention is that improved customization of a cartilage implant for implantation in a human body may be achieved.

Another benefit of the present invention is that a smooth surface is obtained that is perfectly aligned with the remaining healthy cartilage tissue after the implantation procedure.

Another benefit of the present invention is that an accurate three-dimensional representation of the articular surface can be obtained.

Another benefit of the present invention is that an improved set of segmentation process control parameters may be obtained, thus enabling improved manufacturing of patient-customized surgical equipment for cartilage repair.

In one or more embodiments, a method of manufacturing a surgical kit for cartilage repair on an articulating surface of a joint, comprising the steps of:

-receiving radiological image data representing a three-dimensional image of a joint;

-generating a first three-dimensional representation (330,460) of a first surface of a joint (260) in an upgradeable image segmentation process based on the upgraded set of segmentation process control parameters (420) and the radiological image data;

-generating a set of data representing a geometric object based on said first surface, wherein the set of data is limited to said first surface;

-generating control software for controlling a CAD or CAM system to manufacture a surgical kit for cartilage repair from the set of data representing geometric objects and a predetermined model of a component of said surgical kit;

in one or more embodiments, further comprising the step of generating a second three-dimensional representation (320) of a second surface of the joint (230) within the upgradeable dynamic modeling process based on the upgraded set of dynamic modeling process control parameters and the radiological image data; and;

generating a set of data representing a geometric object further based on the second three-dimensional representation, wherein the geometric object is further constrained by the second surface;

in one or more embodiments, the method further comprises the step of generating a cartilage lesion perimeter CDP based on the radiological image data; and;

generating a set of data representing a geometric object further based on the CDP, wherein the geometric object is further constrained by the CDP;

in one or more embodiments, further comprising the steps of generating a surgical equipment circumference SKP based on the radiological image data, and;

generating a set of data representing a geometric object further based on the SKP, wherein the geometric object is further constrained by the SKP;

in one or more embodiments, the step of generating a first three-dimensional representation of a first surface of a joint in a scalable image segmentation process further comprises the steps of:

1) acquiring a segmentation process control parameter set example of a preset ordered group;

2) generating a first three-dimensional representation of the first surface based on the updated first instance of the set of segmentation process control parameters and the radiological image data;

3) storing the first three-dimensional representation in a data buffer;

4) generating a first three-dimensional representation 460 of the first surface based on the updated next instance of the set of segmentation process control parameters and the radiological image data;

5) storing the first three-dimensional representation in a data buffer;

6) repeating steps four and five for all instances of the predetermined ordered set;

7) an updated upgraded set of split process control parameters is determined based on a first three-dimensional performance quality value, wherein the first three-dimensional performance quality value is based on the three-dimensional performance, the predetermined ordered set, and a predetermined objective function stored in a data buffer.

In one or more embodiments, the step of generating the first three-dimensional representation of the first surface of the joint further comprises the steps of:

11) acquiring an initial segmentation process control parameter set;

12) determining an upgraded segmentation process control parameter set as the initial segmentation process control parameter set;

13) generating a first three-dimensional representation of the first surface based on the upgraded set of segmentation process control parameters and radiological image data,

14) determining a set of differentially upgraded segmentation process control parameters based on the first three-dimensional representation quality value, wherein the first three-dimensional representation quality value is based on the three-dimensional representation and a predetermined objective function;

15) determining an updated upgraded set of split process control parameters based on the upgraded set of split process control parameters and the differentially upgraded set of split process control parameters, wherein the first three-dimensional representation quality value is based on the three-dimensional representation and a predetermined objective function;

16) if the three-dimensional representation quality value is below or above a predetermined quality value threshold, steps 13-16 above are repeated.

In one or more embodiments, the step of generating a second three-dimensional representation of a second surface of the joint in a scalable image segmentation process further comprises the steps of:

20) acquiring a process control parameter set of an initial dynamic model;

21) determining an upgraded dynamic model process control parameter set as the initial dynamic model process control parameter set;

22) generating a second three-dimensional representation of the second surface based on the upgraded dynamic model parameter control group and the radiological image data;

23) determining a differentially upgraded dynamic model process control group based on a three-dimensional performance quality value, wherein the three-dimensional performance quality value is based on the three-dimensional performance;

24) determining an updated dynamic model process control parameter set based on the updated dynamic model process control parameter set and the differentially updated dynamic model process control parameter set;

25) if the three-dimensional representation quality value is below or above a predetermined quality value threshold, steps 20-25 above are repeated.

In one or more embodiments, wherein the radiological image data is based on X-ray, ultrasound, Computed Tomography (CT), nuclear medicine, Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI).

In one or more embodiments, an implant design center system for manufacturing surgical equipment for cartilage repair on an articulating surface of a joint, the system comprising:

a memory 830;

a communication interface 840;

a processor 810 configured to perform the steps of:

-receiving radiological image data representing a three-dimensional image of a joint;

-generating a first three-dimensional representation of a first surface of the joint in an upgradeable image segmentation process based on the upgraded set of segmentation process control parameters and the radiological image data;

-generating a set of data representing a geometric object based on said first surface, wherein said geometric object is constrained to said first surface;

-generating control software adapted to control a CAD or CAM system to manufacture a surgical kit for cartilage repair based on the data representing the set of geometric objects and a predetermined model of a component of said surgical kit.

A computer program product comprising computer readable code configured to, when executed in a processor, perform any or all of the method steps of the present invention.

A non-transitory computer readable memory having computer readable code stored thereon, configured to, when executed within a processor, perform any or all of the method steps of the present invention.

Drawings

Embodiments of the invention will be described in detail with reference to the accompanying drawings, in which:

FIG. 1a depicts a healthy joint with bone and cartilage tissue;

FIG. 1b depicts a damaged joint with bone and damaged cartilage tissue;

FIG. 2a depicts a side view cross-section of a generated first three-dimensional representation of a first surface of a joint, a generated second three-dimensional representation of a second surface of a joint, a generated cartilage lesion perimeter CDP, and a surgical equipment perimeter SKP generated in accordance with one or more embodiments of the present disclosure;

FIG. 2b depicts a top view of a generated first three-dimensional representation of a first surface of a joint, a generated second three-dimensional representation of a second surface of a joint, a generated cartilage lesion perimeter CDP, and a surgical equipment perimeter SKP generated in accordance with one or more embodiments of the present disclosure;

FIG. 3a shows a schematic diagram of an embodiment of a first three-dimensional representation of a first surface of a joint, a second three-dimensional representation of a second surface of a joint, and a geometric object;

FIG. 3b shows a schematic view of an embodiment of an implant assembly comprising an implant component adapted within a model of an assembly of a geometric object based surgical equipment, and an implant guiding member comprising an implant guiding component adapted within an assembled model of a geometric object based surgical equipment,

FIG. 4 shows a schematic diagram of an embodiment of a scalable image segmentation process;

FIG. 5 shows a schematic diagram of an alternative embodiment of a scalable image segmentation process;

FIG. 6 shows a schematic diagram of an embodiment of a scalable dynamic model process;

FIG. 7 shows a schematic view of an embodiment of a system for manufacturing a surgical kit for cartilage repair;

fig. 8 shows a schematic view of an embodiment of an implant design center suitable for generating control software suitable for controlling a CAD or CAM system for manufacturing a surgical kit for cartilage repair;

FIG. 9 shows a flow diagram of an embodiment of a computer-implemented method for manufacturing a surgical kit;

FIG. 10 shows a flow diagram of an embodiment of another computer-implemented method of manufacturing a surgical kit for cartilage repair of an articulating surface of a joint;

FIG. 11 shows a flow diagram of an embodiment of another computer-implemented method of manufacturing a surgical kit for cartilage repair of an articulating surface of a joint;

FIG. 12 shows a flow diagram of an embodiment of another computer-implemented method of manufacturing a surgical kit for cartilage repair of an articulating surface of a joint;

FIG. 13 shows a flow chart of an embodiment of another computer-implemented method of manufacturing a surgical kit for cartilage repair of an articulating surface of a joint.

Detailed Description

Introduction to

When cartilage is repaired by performing a cartilage implant surgery, a first problem is to determine the design or size of an implant. The implant is typically connected to the patient's body by inserting the implant support within a drilled cavity (e.g., bone). Another problem is therefore to ensure that the cartilage implant is correctly placed or positioned, i.e. that the position and mounting angle of the implant is correct, thereby aligning the outer or top surface of the implant with the remaining cartilage tissue.

The inventive concepts described herein address the above-mentioned problems by providing an implant and a matching implant guide, referred to herein as a surgical rig, based on radiographic image data. The implant guide can be placed on and connected to the patient's body, typically cartilage-attached bone, thereby allowing drilling of the cavity in the correct position and allowing the installation angle for insertion of the implant support.

To customize the surgical equipment for a particular patient, the design of the surgical equipment is obtained by employing an assembly model that includes an implant assembly and an implant guide assembly. The contour of the implant is determined based on the cartilage damage perimeter CDP, which parameter indicates the extent of the cartilage damage, e.g., the second surface of the joint. The contour of the implant guide is determined by generating a surgical rig perimeter SKP that specifies a region on a first surface of the joint, such as underlying bone available on a particular patient, for placement of the implant guide. The outer surface or top surface of the implant body is defined by the outer surface of the remaining cartilage tissue and is adapted to obtain a smooth transition between the outer cartilage surface and the outer surface of the implant body, i.e. to be in alignment with the outer surface of the remaining cartilage tissue. The component model is adapted based on the first surface of the joint and optionally the second surface of the joint, the CDP and the SKP.

To apply the model assembly, a first three-dimensional representation of a first surface of the joint, optionally a second three-dimensional representation of a second surface of the joint, the CDP and optionally the SKP are generated based on the radiographic image data.

To extract the first surface from the radiological image data, a first three-dimensional representation of the first surface may be generated within an image segmentation process based on a set of segmentation process control parameters.

The outer or top surface of the implant replaces the damaged tissue. The outer or top surface of the damaged tissue is not identifiable within the radiological image data, then the undamaged tissue is identifiable and can be used as a dynamic model for the second surface to align with the undamaged tissue. Thus, based on the set of dynamic model process control parameters, a second three-dimensional representation of the second surface of the joint may be generated within the dynamic model process.

After the first three-dimensional representation, optionally the second three-dimensional representation, the CDP and the SKP are generated, a set of data representing geometric objects may be generated and used to generate control software suitable for controlling a CAD or CAM system to manufacture surgical equipment for cartilage repair.

Definition of

A surgical kit for cartilage repair is herein understood to be a set of customized implants and implant guides adapted for an individual or patient, in preparation for an implant surgery, wherein the implant guides are configured to assist the surgeon in the implant surgery by guiding the implant to a desired position and at the correct angle of installation on the individual of the subject of the implant surgery. If the implant deviates from its intended position, this can cause increased wear and loading on the joint. A difficult task for a surgeon is to place or position an implant. A perfectly matched implant and tools designed to assist the surgeon in the implant procedure are therefore needed. The design of the implant and the guide, in other words the design of the surgical equipment, is critical to the outcome of the lifetime of the implant in the patient's joint. Minor differences in placement or implant design can make a great difference in the benefit and longevity of the implant within the patient, the recovery time of the patient, the economic value due to the time of the procedure, the success rate of the implant procedure.

The surface of a joint is understood herein to be a surface associated with cartilage, for example the inner surface of cartilage connected to the outer surface of bone or cartilage.

Radiological image data, representing a three-dimensional image of a joint, is understood herein to be data arranged in a three-dimensional array of voxels (voxels), or pixels, or a plurality of sequential two-dimensional images obtained within a predetermined distance. The pixels comprise pixel values within a predetermined resolution, such as 8,16,32 bits, wherein the pixel values may indicate intensity or grey values. In one example, the radiological image data is shaped in a 3D array of voxels or pixels. Each voxel may have an intensity or gray value, e.g., ranging from 0 to 65535 for 16-bit pixels and 0 to 255 for 8-bit pixels. Most medical image systems generate images using a 16-bit gray scale range. Three-dimensional images typically have a large number of pixels and are very computationally intensive for processing such as segmentation and pattern recognition. To reduce complexity and intensive computations, a three-dimensional representation of the first surface may be generated in a process such as segmentation.

A three-dimensional representation is herein understood to be a data structure arranged in an array of data values representing a three-dimensional surface, for example. Such a three-dimensional surface may be a representation of a voxel, a three-dimensional mesh, a parametric surface, a surface model, or any other three-dimensional surface known to those skilled in the art selected from three-dimensional radiological image data.

A scalable process is herein understood to be a process associated with input data, such as image data, geometric models and control parameters, output data, such as a three-dimensional representation, and an upgrade unit configured to evaluate the output data by generating a quality value and iterating the process based on the quality value. The iterations may continue until the quality value exceeds a predetermined threshold.

An image segmentation process is herein understood to be a process of configuring to the segmented image data in a semantically meaningful way to generate a three-dimensional representation, e.g. identifying the articulation surface of a joint, e.g. the inner surface of cartilage connected to a bone, based on dynamic segmentation process control parameters and radiological image data.

A set of segmentation process control parameters is herein understood to be control parameters that control the nature of the image segmentation process, and the three-dimensional representation generated by the image segmentation process. Examples of such parameters may be expected hole radius, number of categories identifying different regions or tissue types, fault tolerance of clusters, classification into different categories of gray values, smoothness of regions in classification, number of iterations, down-sampling coefficients or spline distances.

A dynamic model process may be understood herein as a process configured to adapt a dynamic model of a surface to align with radiological image data, such as a perimeter of intact cartilage tissue or a predetermined feature of an estimated surface of intact cartilage, to generate a second three-dimensional representation based on dynamic model process control parameters and the radiological image data.

Dynamic model control parameters are herein understood to be control parameters that control the nature of the dynamic model process and the generated three-dimensional representation.

Perimeter is understood herein to be the perimeter that defines a subset of the three-dimensional representation.

Cartilage damage Circumference (CDP) is understood herein as the circumference defining a subset of the first or second three-dimensional representation. In one example, it may include identifying a circumference of the implant and a portion of the surgical equipment to be manufactured.

Surgical equipment circumference (SKP) may be understood herein as the circumference defining a subset of the first three-dimensional representation. In one example, it may include identifying a feasible location of the guide and a perimeter of a portion of the surgical equipment to be manufactured.

A geometric object is herein understood to be a geometric object or a volume bounded by a subset of a first three-dimensional representation as a bottom, by a second three-dimensional representation as a top and a surface interconnecting the top and the bottom of the geometric object. A subset of the first or second three-dimensional representations is defined by a perimeter, such as a CDP or surgical equipment perimeter SKP.

Control software adapted for controlling a CAD or CAM system is herein understood to be data values represented by a data structure comprising computer program code portions configured to direct a processor to perform the display or to direct the manufacture of surgical equipment for cartilage repair at an articulating surface of a joint.

A predetermined model of the component is understood herein to be a model comprising an implant component and an implant guide component, which may be adapted based on the geometric object, the radiological image data, the first three-dimensional representation and the second three-dimensional representation. In one example, the implant assembly is sized to replace damaged cartilage, and the implant assembly is adapted to match the first and second three-dimensional representations to achieve a fixation point on the first representation or bone, to achieve proper positioning of the implant assembly, and to achieve a proper installation angle of the implant assembly.

A three-dimensional representation quality value is understood herein to be a quality value determined over a predetermined relationship based on a three-dimensional representation. In one example, this may include comparing the three-dimensional representation to a reference surface, for example, by generating a measure of the distance between surfaces in Euclidean space. In another example, it may include evaluating smoothness of the three-dimensional representation or deviation of the three-dimensional representation compared to a reference surface.

Drawings

Fig. 1a depicts a healthy joint 110 having bone 130 and cartilage tissue 120.

Fig. 1b depicts a damaged joint 140 having bone 150 and damaged cartilage tissue 160.

Fig. 2a depicts a side cross-sectional view of a generated first three-dimensional representation of a first surface 260 of a joint, a generated second three-dimensional representation of a second surface of a joint 230, a generated cartilage lesion perimeter CDP250, and a generated surgical equipment perimeter SKP270, in accordance with one or more embodiments of the present disclosure.

Fig. 2b depicts a top view of the generated three-dimensional representation of the first surface of the joint 260, the generated cartilage lesion perimeter 250, and the generated plurality of surgical equipment perimeters SKP270, in accordance with one or more embodiments of the present disclosure.

Fig. 3a shows a schematic diagram of a generated first three-dimensional representation 330 of a first surface of a joint, a generated second three-dimensional representation 320 of a second surface of a joint and a geometric object 350. In one or more embodiments, the geometric object is generated based on the CDP, the first three-dimensional representation, and the second three-dimensional representation. In one or more embodiments, the first three-dimensional representation 330 is represented in the form of selected voxels within the radiological image data, a polygonal mesh, an anatomical model, or a parametric curve.

In one non-limiting example, an implant mounting location is determined as a location on the first three-dimensional representation included in the CDP and an implant mounting angle is determined as an orthogonal or surface normal vector of the first three-dimensional representation. A first subset of the first three-dimensional representation is determined based on the CDP. The CDP is further converted to a second three-dimensional representation along an axis represented by the implant installation angle, and a second subset of the first three-dimensional representation is determined based on the converted CDP. The geometric object is then generated based on the first subset, the second subset, and a surface that inscribes the first subset and the second subset.

Fig. 3b depicts a surgical instrument manufactured based on control software adapted to control a CAD or CAM system to manufacture a surgical instrument based on a set of data representing geometric objects for cartilage repair, and a predetermined model of components of the surgical instrument according to the disclosed method. In one or more embodiments, the predetermined model of the assembly includes an implant assembly 350 and an implant guide assembly 360. In one or more embodiments, the predetermined model of the assembly further comprises an implant support assembly 370. In one or more embodiments, the implant assembly is adapted based on the geometric objects. In one or more embodiments, the implant guide assembly is adapted based on the geometric objects and SKP.

In one non-limiting example, the implant assembly is adapted by scaling and rotating the implant assembly so that it is included within the geometric object and the top or outer surface of the implant assembly is aligned with the geometric object and with the undamaged cartilage assembly.

In order to design and manufacture customized surgical equipment for cartilage repair on the articulating surfaces of a joint, it is necessary to identify reference surfaces, such as bone, underlying the cartilage tissue. The reference surface is identified by extracting the first surface from the radiological image data, for example by generating a first three-dimensional representation of the first surface during a radiological image segmentation process based on a set of segmentation process control parameters. The quality or accuracy of the generated first three-dimensional representation or surface depends on the set of segmentation process control parameters and may differ for individual patients and for individual types of joints, such as knee, toe, elbow, etc. For further quality and accuracy of the first three-dimensional representation, the set of segmentation process control parameters may be obtained by parameter upgrade. Parameter upgrading typically includes attempting to match certain models, such as an objective function, to observed data, such as a first three-dimensional representation of a first surface.

FIG. 4 shows a schematic diagram of an embodiment of a method for scalable image segmentation process according to the present disclosure. In one or more embodiments, the first three-dimensional representation 460 of the first surface of the joint is generated in the upgradeable image segmentation process 450 based on the upgraded set of compartmentalized process control parameters 430 and the radiological image 410. In one or more embodiments, the method comprises:

1) acquiring a segmentation process control parameter example of a preset ordered group;

2) generating a first three-dimensional representation 460 of the first surface based on the updated set of segmentation process control parameters and the radiological image data;

3) storing the first three-dimensional representation in a memory or data buffer 480;

4) generating a first three-dimensional representation 460 of the first surface based on the updated set of segmentation process control parameters and the next instance of the radiological image data;

5) storing the first three-dimensional representation in a data buffer 480;

3) repeating steps 1 and 2 for all instances of said predetermined ordered set;

4) an updated set of segmentation process control parameters is determined 490 based on a first three-dimensional performance quality value based on the three-dimensional performance stored in the data buffer 480, the predetermined ordered set, and the predetermined objective function.

In one non-limiting example, a predetermined number of instances of the segmentation process control parameters are obtained as an ordered set, e.g., ten previously used sets of parameters for the segmentation process. A first three-dimensional representation is generated for each instance of the split process control parameter and stored in a data buffer or memory. An objective function, e.g. a measure of how a segmented surface deviates from a reference surface, e.g. a parametric surface, is used to determine a first three-dimensional quality representation value. An updated set of split process control parameters is then determined, for example, to select an instance from the ordered set based on the first three-dimensional performance quality value, to join instances from the ordered set based on the first three-dimensional performance quality value, or to generate a new instance of the updated set of split process control parameters. In alternative embodiments, any objective function known to those skilled in the art may be used, such as roughness measurements for three-dimensional surfaces/meshes.

FIG. 5 is a schematic diagram illustrating an embodiment of a scalable segmentation process in accordance with the disclosed method. In one or more embodiments, the first three-dimensional representation 560 of the first surface of the joint in the upgradeable image segmentation process 550 is based on the upgraded segmentation process control parameter set 530 and the radiological image data 510. In one or more embodiments, the method further comprises:

1) obtaining an initial segmentation process control parameter set 520;

2) determining an updated set of segmentation process control parameters 530 as the initial set of segmentation process control parameters 520;

3) generating a first three-dimensional representation 560 of the first surface based on the updated set of segmentation process control parameters and the radiological image data;

4) determining 570 an upgraded set of segmentation process control parameters 540 based on the first three-dimensional performance quality value;

5) determining an updated upgraded set of split process control parameters 530 based on the upgraded set of split process control parameters 520 and the differential upgraded set of split process control parameters 540;

6) and if the first three-dimensional expression quality value is lower or higher than a preset quality value threshold value, repeating the steps 3-6.

In one non-limiting example, an initial set of split process control parameters is obtained, such as a previously used and stored set of split process control parameters. The initial set of segmentation process control parameters is determined as updated segmentation process control parameters and used in conjunction with the received radiological image data, and is determined as a description of the joints within the updated set of segmentation process control parameters to generate a first three-dimensional representation. The generated first three-dimensional representation is evaluated by a predetermined objective function, e.g. a measure of how the segmented surface deviates from a reference surface, to obtain a first three-dimensional representation quality value. The set of differential upgraded split process control parameters is determined based on the first three-dimensional performance quality value, for example by applying a predetermined incremental value to the set of upgraded split process control parameters or by performing a look-up within a predetermined table comprising the set of upgraded split process control groups and the first three-dimensional performance quality value. An updated upgraded set of split process control parameters is further determined based on the differentially upgraded set of split process control parameters and the upgraded set of split process control parameters. The method steps may be repeated until it may be determined that the first three-dimensional performance quality value is below, above, or equal to a predetermined quality value threshold.

FIG. 6 is a schematic diagram illustrating one embodiment of a scalable dynamic model process in accordance with the disclosed method. In one or more embodiments, the second three-dimensional representation 660 of the second surface of the joint within the upgradeable dynamic model process 650 is dependent on the upgraded set of dynamic model control parameters 630 and the radiological image data 610. In one or more embodiments, the method further comprises:

10) obtaining an initial dynamic model process control parameter set 620;

11) determining an upgraded dynamic model process control parameter set 630 as the initial dynamic model process control parameter set 620;

12) generating a second three-dimensional representation 660 of the second surface based on the upgraded set of dynamic model process control parameters and the radiological image data 610;

13) determining 670 a set of differentially upgraded dynamic model process control parameters 640 based on a three-dimensional representation quality value, wherein the three-dimensional representation quality value is based 660 on the three-dimensional representation;

14) determining an updated set of dynamic model process control parameters 630 based on the updated set of dynamic model process control parameters 620 and the differentially updated set of dynamic model process control parameters 640;

15) if the three-dimensional representation quality value is below or above a predetermined quality value threshold, repeating steps 10-15 above.

In one or more embodiments, wherein generating the second three-dimensional representation is further based on a dynamic model, such as an anatomical model or a parametric surface.

In one non-limiting example, the initial set of dynamic model process control parameters is obtained as a set of previously used and stored dynamic model process control parameters, for example. The initial set of dynamic model process control parameters is determined as an updated set of dynamic model process control parameters for use with the received radiological image data and as a description of a joint within the updated dynamic model process to generate a second three-dimensional representation. The generated second three-dimensional representation is evaluated by a predetermined objective function, e.g., a measure of how the generated second three-dimensional representation deviates from a reference surface, to obtain a second three-dimensional representation quality value. A set of differentially upgraded dynamic model process control parameters is determined based on the second three-dimensional performance quality value, for example by applying a predetermined incremental value to the set of upgraded dynamic model process control parameters, or by performing a lookup within a predetermined table that includes the set of upgraded dynamic model process control parameters and the second three-dimensional performance quality. An updated set of upgraded dynamic model process control parameters is further determined based on the set of differentially upgraded dynamic model process control parameters and the set of upgraded dynamic model process control parameters. The method may be repeated until it may be determined that the second three-dimensional performance quality value is below, above, or equal to a predetermined quality value threshold.

Fig. 7 shows a schematic view of an embodiment of a system for manufacturing a surgical kit for cartilage repair. In one or more embodiments, the system includes a diagnostic hub 710 having a diagnostic processor 713, the diagnostic processor 713 being configured to receive a user indication as diagnostic data indicative of cartilage damage and to receive information about the patient 714 via a user input/output device 711. The diagnostic processor 713 is further configured to transmit the diagnostic data to the radiological image center 720 via the communication network 740. The radiological image center 720 further includes an image processor 723 configured to present diagnostic data to a user for acquiring radiological image data from a radiological image device 724, such as a CT or MR scanner, and to transmit the diagnostic data and the radiological image data as signals over a communication network 740 to an implant design center. The implant design center includes a processor 733 configured to receive and optionally store diagnostic data and radiological image data to a memory 732. The processor 733 is further configured to generate a first three-dimensional representation of a first surface of the joint in an upgradeable image segmentation process based on an upgraded set of segmentation process control parameters and the radiological image data, and generate a second three-dimensional representation of a second surface of the joint in an upgradeable dynamic modeling process based on an upgraded set of dynamic model process control parameters and the radiological image data; generating a cartilage injury perimeter CDP based on said radiological image data, generating a set of data representing a geometric object based on said first surface, said second surface and said CDP, wherein said geometric object represents an identified cartilage injury, wherein said geometric object is constrained to said first surface, said second surface and said CDP, generating control software adapted to control a CAD or CAM system to manufacture surgical equipment for cartilage repair based on the set of data representing geometric objects and a predetermined model of a component of said surgical equipment. The processor 733 is further configured to send the control software to an implant production center 740. The implant production center 740 includes a processor 743 configured to receive the control software and optionally store the diagnostic data and radiological image data in memory 742. The processor 743 is further configured for controlling the production line to manufacture a surgical kit for cartilage repair based on said received control software.

Fig. 8 shows a schematic view of an embodiment of an implant design center adapted to generate control software for controlling a CAD or CAM system for manufacturing a surgical kit for cartilage repair. In one or more embodiments, the implant design center is in the form of, for example, a tablet, laptop, or desktop computer. The implant design center is configured to generate control software suitable for controlling a CAD or CAM system to manufacture surgical equipment for cartilage repair. The design center further comprises a processor/processing unit 810, which processor/processing unit 810 is provided with a specially designed program or program code portions for controlling the processing unit 810 to perform the steps and functions of the embodiments of the method according to the invention. The computer system further comprises at least one memory 830 configured to store data values or parameters received from the processor 810, or to retrieve and send data values or parameters to the processor 810. In one or more embodiments, the design center further includes a display configured to receive signals from the processor 810 and configured to display the received signals as a displayed image, for example, to a user of the design center. In one or more embodiments, the design center further includes a user input device 825 configured to receive instructions from a user and generate user indicative data, thereby enabling user communication at the implant design center. The user input device 825 is further configured to send the generated data as a signal to the processor 810. The computer system in one or more embodiments further includes a communication interface 840 configured to transmit or receive data values or parameters from the processor 810 to/from external units via the communication interface 840. In one or more embodiments, communication interface 840 is configured to communicate over a communication network.

Further examples of the invention

Fig. 9 shows a flow diagram of a computer-implemented method of manufacturing a surgical kit for cartilage repair at an articulating surface of a joint. In one or more embodiments, the method comprises the steps of:

receiving 910 radiological image data representing a three-dimensional image of a joint;

generating 920 a first three-dimensional representation of a first surface of a joint within a scalable image segmentation process based on the upgraded set of segmentation process control parameters and the radiological image data;

generating 950 a set of data representing a geometric object based on the first surface, wherein the geometric object is constrained to the first surface;

based on the set of data representing the geometric object and the predetermined model of the component of the surgical equipment, control software adapted to control a CAD or CAM system to manufacture the surgical equipment for cartilage repair is generated 970.

FIG. 10 shows a flow chart of a computer-implemented method of manufacturing a surgical kit for cartilage repair at an articulating surface of a joint. In one or more embodiments, the method comprises the steps of:

receiving 1010 radiological image data representing a three-dimensional image of a joint;

generating 1020 a first three-dimensional representation of a first surface of a joint in an upgradeable dynamic model process based on the upgraded set of dynamic model process control parameters and the radiological image data;

generating 1030 a second three-dimensional representation of a second surface of the joint in an upgradeable dynamic modeling process based on the upgraded set of dynamic modeling process control parameters and the radiological image data;

generating 1060 a set of data representing a geometric object based on the first surface and the second surface, wherein the geometric object is constrained to the first surface and the second surface;

based on the set of data representing the geometric objects and the predetermined model of the components of the surgical equipment, control software suitable for controlling a CAD or CAM system to manufacture the surgical equipment for cartilage repair is generated 1070.

FIG. 11 shows a flow chart of a computer-implemented method of manufacturing a surgical kit for cartilage repair at an articulating surface of a joint. In one or more embodiments, the method comprises the steps of:

receiving 1110 radiological image data representing a three-dimensional image of a joint;

generating 1120 a first three-dimensional representation of a first surface of a joint in an upgradeable image segmentation process based on the upgraded set of segmentation process control parameters and the radiological image data;

generating 1130 a second three-dimensional representation of a second surface of the joint in an upgradeable dynamic model process based on the upgraded set of dynamic model process control parameters and the radiological image data;

generating 1140 a cartilage lesion perimeter CDP based on the radiological image data;

generating 1160 a set of data representing a geometric object based on the first surface, second surface and the CDP, wherein the geometric object represents the identified cartilage damage, wherein the geometric object is constrained by the first surface, the second surface and the CDP;

based on the set of data representing the geometric object and the predetermined model of the components of the surgical equipment, control software adapted for controlling a CAD or CAM system to manufacture the surgical equipment for cartilage repair is generated 1170.

FIG. 12 shows a flow chart of a computer-implemented method of manufacturing a surgical kit for cartilage repair at an articulating surface of a joint. In one or more embodiments, the method comprises the steps of:

receiving 1210 radiological image data representing a three-dimensional image of a joint;

generating 1220 a first three-dimensional representation of a first surface of a joint in an upgradeable image segmentation process based on the upgraded set of segmentation process control parameters and the radiological image data;

generating 1230 a second three-dimensional representation of a second surface of the joint in an upgradeable dynamic modeling process based on the upgraded set of dynamic model process control parameters and the radiological image data;

generating 1250 a surgical rig circumference SKP based on the radiological image data;

generating 1260 a set of data representing a geometric object based on the first surface, second surface, and the CDP, wherein the geometric object represents the identified cartilage lesion, wherein the geometric object is constrained by the first surface, the second surface, and the SKP;

based on the set of data representing the geometric object and the predetermined model of the component of the surgical equipment, control software adapted to control the CAD or CAM system to manufacture the surgical equipment for cartilage repair is generated 1270.

FIG. 13 shows a flow chart of a computer-implemented method of manufacturing a surgical kit for cartilage repair at an articulating surface of a joint. In one or more embodiments, the method comprises the steps of:

receiving 1310 radiological image data representing a three-dimensional image of a joint;

generating 1320 a first three-dimensional representation of a first surface of a joint in an upgradeable image segmentation process based on the upgraded set of segmentation process control parameters and the radiological image data;

generating 1330 a second three-dimensional representation of a second surface of the joint in an upgradeable dynamic model process based on the upgraded set of dynamic model process control parameters and the radiological image data;

generating 1340 a cartilage lesion perimeter CDP based on the radiological image data;

generating 1350 a surgical equipment circumference SKP based on the radiographic image data;

generating 1360 a set of data representing a geometric object based on the first surface, second surface, the CDP, and the SKP, wherein the geometric object represents the identified cartilage lesion, wherein the geometric object is constrained by the first surface, the second surface, the CDP, and the SKP;

based on the set of data representing the geometric object and the predetermined model of the component of the surgical equipment, generating 1370 control software suitable for controlling a CAD or CAM system to manufacture the surgical equipment for cartilage repair.

In one or more embodiments, the implant design center includes a processing unit (e.g., a processor, microcontroller, or other circuitry or an integrated circuit that can execute instructions to perform various processing operations).

In one or more embodiments, an implant design center having a non-transitory computer readable medium with computer readable code stored thereon, which when executed by a processor of a remote inspection system, causes the processor to perform the methods of the present invention.

In one or more embodiments, a computer programming product comprising computer readable code is configured to, when executed in a processor, perform any or all of the method steps of the invention.

Where applicable, various embodiments of the present disclosure can be implemented in hardware, software, or a combination of hardware and software. Additionally, where applicable, the various hardware components and/or software components set forth herein can be combined in various combinations including software, hardware, and/or software and hardware without departing from the spirit of the present invention. Where applicable, the various hardware components and/or software components set forth herein can be separated into sub-components comprising software, hardware, or a combination of both without departing from the spirit of the present invention. Further, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.

Software, such as non-transitory instructions, program code, and/or data, disclosed in accordance with the present invention may be stored in one or more non-transitory computer-readable media. It is contemplated that the software identified in the present invention may be used with one or more general purpose or special purpose networked and/or otherwise computer and/or computer systems. Where applicable, the ordering of various steps described herein can be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.

The described embodiments of the invention are not intended to be limiting. It will be understood that numerous modifications and variations are possible in accordance with the principles of the present invention. Accordingly, the scope of the invention is defined only by the claims.

Claims (13)

1. A method of generating a three-dimensional representation of a joint, comprising the steps of:

-receiving (910) radiological image data (410,510,610) representing an image of a joint in three dimensions;

-generating (920) a first three-dimensional representation (330,460) of a first surface of a joint (260) within an upgradeable image segmentation process based on an upgraded set of segmentation process control parameters (420) and the radiological image data (410,510,610), wherein the upgradeable image segmentation process comprises generating a first three-dimensional representation quality value,

wherein the image segmentation process is scalable and configured to generate a quality value and to perform an iterative process based on the quality value, the iterative process continuing until the quality value exceeds a predetermined threshold.

2. The method as recited in claim 1, further comprising generating (950) a set of data representing a geometric object based on the first surface, wherein the geometric object represents an identified lesion and is constrained by the first surface.

3. The method of claim 1, further comprising the step of generating a second three-dimensional representation (320) of a second surface of the joint (230) within the upgradeable dynamic model process based on the upgraded set of dynamic model process control parameters and the radiological image data (410,510,610).

4. The method of claim 2 or 3, further comprising generating a set of data representing a geometric object further based on the second three-dimensional representation, wherein the geometric object is further constrained by the second surface.

5. The method of claim 2, further comprising the step of generating a cartilage lesion perimeter CDP based on said radiological image data (410,510,610); and

further based on the CDP, generating a set of data representing a geometric object, wherein the geometric object is further constrained by the CDP.

6. The method of claim 2, further comprising the step of generating a surgical rig circumference SKP based on the radiological image data (410,510,610); and

generating a set of data representing a geometric object further based on the SKP, wherein the geometric object is constrained by the SKP.

7. The method of claim 1, wherein generating the first three-dimensional representation (330,460) of the first surface of the joint (260) in a scalable image segmentation process further comprises the steps of:

1) acquiring a segmentation process control parameter example of a preset ordered group;

2) generating a first three-dimensional representation of a first surface based on the upgraded set of segmentation process control parameters and the radiological image data (410,510,610);

3) storing the first three-dimensional representation in a data buffer;

4) generating a first three-dimensional representation (460) of a first surface based on the updated next instance of the set of segmentation process control parameters and the radiological image data (410,510,610);

5) storing the first three-dimensional representation in a data buffer;

6) repeating steps four and five for all instances of the predetermined ordered set;

7) an updated upgraded set of split process control parameters is determined based on a first three-dimensional performance quality value, wherein the first three-dimensional performance quality value is based on the first three-dimensional performance stored in a data buffer, the predetermined ordered set and a predetermined objective function.

8. The method of claim 1, wherein generating a first three-dimensional representation of a first surface of a joint (260) within a scalable image segmentation process further comprises the steps of:

11) acquiring an initial segmentation process control parameter set;

12) determining an upgraded segmentation process control parameter set as the initial segmentation process control parameter set;

13) generating a first three-dimensional representation of the first surface based on the upgraded set of segmentation process control parameters and radiological image data (410,510,610),

14) determining a set of differentially upgraded segmentation process control parameters based on the first three-dimensional representation quality value, wherein the first three-dimensional representation quality value is based on the first three-dimensional representation and a predetermined objective function;

15) determining an updated upgraded split process control parameter set based on the upgraded split process control parameter set and the differentially upgraded split process control parameter set;

16) if the first three-dimensional representation quality value is below or above a predetermined quality value threshold, repeating steps 11-15 above.

9. The method of claim 3, wherein generating a second three-dimensional representation of a second surface of a joint within a scalable image segmentation process further comprises the steps of:

20) acquiring a process control parameter set of an initial dynamic model;

21) determining an upgraded dynamic model process control parameter set as the initial dynamic model process control parameter set;

22) generating a second three-dimensional representation of the second surface based on the upgraded set of dynamic model process control parameters and the radiological image data (410,510,610);

23) determining a set of differentially upgraded dynamic model process control parameters based on a second three-dimensional representation quality value, wherein the second three-dimensional representation quality value is based on the second three-dimensional representation;

24) determining an updated dynamic model process control parameter set based on the updated dynamic model process control parameter set and the differentially updated dynamic model process control parameter set;

25) if the second three-dimensional performance quality value is below or above a predetermined quality value threshold, steps 20-24 above are repeated.

10. The method of claim 1, wherein the radiological image data (410,510,610) is based on X-ray, ultrasound, Computed Tomography (CT), nuclear medicine, Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI).

11. A system for generating a three-dimensional representation of a joint, the system comprising:

a memory (830);

a communication interface (840);

a processor (810) configured to perform the steps of:

-receiving radiological image data (410,510,610) representing an image of a joint in three dimensions;

-generating a first three-dimensional representation of a first surface of a joint (260) in an upgradeable image segmentation process based on an upgraded set of segmentation process control parameters and the radiological image data (410,510,610), wherein the upgradeable image segmentation process comprises generating a three-dimensional representation quality value,

wherein the image segmentation process is scalable and configured to generate a quality value and to perform an iterative process based on the quality value, the iterative process continuing until the quality value exceeds a predetermined threshold.

12. The system of claim 11, wherein the processor (810) is further configured to perform the step of generating a set of data representing geometric objects based on the first surface, wherein the several objects represent identified lesions and are constrained by the first surface.

13. The system of claim 11, wherein the processor 810 is further configured to perform the steps of any of claims 2-10.