METHOD AND NODE FOR MANUFACTURING A SURGICAL KIT FOR CARTILAGE REPAIR

1 : A method of manufacturing a surgical kit for cartilage repair in an articulating surface of a joint, comprising the steps of:

receiving radiology image data representing three dimensional image of a joint;

generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process dependent on a trained segmentation process control parameter set and said radiology image data, wherein the trainable image segmentation process comprises generating a three dimensional representation quality value;

generating a set of data representing a geometrical object based on said first surface, wherein said geometrical object is confined by said first surface; and

generating control software adapted to control a CAM system to manufacture a surgical kit for cartilage repair dependent on said set of data representing a geometrical object and on a predetermined model of components of said surgical kit.

2 : The method of claim 1 , further comprising the step generating a second three dimensional representation of a second surface of the joint in a trainable dynamical model process dependent on a trained dynamical model process control parameter set and said radiology image data; and

generating a set of data representing a geometrical object further based on said second three dimensional representation, wherein said geometrical object is further confined by said second surface.

3 : The method of claim 1 , further comprising the step generating a cartilage damage perimeter CDP based on said radiology image data; and

generating a set of data representing a geometrical object further based on said CDP, wherein said geometrical object is further confined by said CDP.

4 : The method of claim 1 , further comprising the step generating a surgical kit perimeter SKP based on said radiology image data; and;

generating a set of data representing a geometrical object further based on said SKP, wherein said geometrical object is further confined by said SKP.

5 : The method of claim 1 , wherein generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process further comprises the steps:

I) obtaining a predefined ordered set of segmentation process control parameters instances;

II) generating a first three dimensional representation of said first surface based on the first instance of said trained segmentation process control parameter set and said radiology image data;

III) storing said first three-dimensional representation in a data buffer;

IV) generating a first three dimensional representation of said first surface based on the next instance of said trained segmentation process control parameter set and said radiology image data;

V) storing said first three-dimensional representation in a data buffer;

VI) repeating steps IV and V for all instances of said predefined ordered set; and

VII) determining an updated trained segmentation process control parameter set based on a first three dimensional representation quality value, wherein said first three dimensional representation quality value is based on said three dimensional representations stored in the data buffer, said predefined ordered set and a predetermined object function.

6 : The method of claim 1 , wherein generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process further comprises the steps:

I) obtaining an initial segmentation process control parameter set;

II) determining a trained segmentation process control parameter set as said initial segmentation process control parameter set;

III) generating a first three dimensional representation of said first surface based on said trained segmentation process control parameter set and said radiology image data;

IV) determining a differential trained segmentation process control parameter set based on a first three dimensional representation quality value, wherein said first three dimensional representation quality value is based on said three dimensional representation and a predetermined object function;

V) determining an updated trained segmentation process control parameter set based on said trained segmentation process control parameter set and said differential trained segmentation process control parameter set; and

VI) repeating steps III-VI above if said first three dimensional representation quality value is below or above a predefined quality value threshold.

7 : The method of claim 2 , wherein generating a second three dimensional representation of a second surface of the joint in a trainable image segmentation process further comprises the steps:

I) obtaining an initial dynamical model process control parameter set;

II) determining a trained dynamical model process control parameter set as said initial dynamical model process control parameter set;

III) generating a second three dimensional representation of said second surface based on said trained dynamical model process control parameter set and said radiology image data;

IV) determining a differential trained dynamical model process control parameter set based on a three-dimensional representation quality value, wherein said three dimensional representation quality value is based on said three-dimensional representation;

V) determining an updated trained dynamical model process control parameter set based on said trained dynamical model process control parameter set and said differential trained dynamical model process control parameter set; and

VI repeating steps I-V above if said three dimensional representation quality value is below or above a predefined quality value threshold.

8 : The method of claim 1 , wherein said radiology image data is based on a selection of X-ray, ultrasound, computed tomography (CT), nuclear medicine, positron emission tomography (PET) and magnetic resonance imaging (MRI).

9 : An implant design center system for manufacturing a surgical kit for cartilage repair in an articulating surface of a joint, the system comprising:

a memory;

a communications interface;

a processor configured to perform the steps of:

receiving radiology image data representing three dimensional image of a joint;

generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process dependent on a trained segmentation process control parameter set and said radiology image data, wherein the trainable image segmentation process comprises generating a three dimensional representation quality value;

generating a set of data representing a geometrical object based on said first surface, wherein said geometrical object is confined by said first surface; and

generating control software adapted to control a CAM system to manufacture a surgical kit for cartilage repair dependent on said set of data representing a geometrical object and on a predetermined model of components of said surgical kit.

10 . (canceled)

11 : A computer program product comprising computer readable code configured to, when executed in a processor, perform the method steps of claim 1 .

12 : A non-transitory computer readable memory on which is stored computer readable code configured to, when executed in a processor, perform the method steps of claim 1 .