Method for simulating a device deployment

A computer-implemented method for training a machine learning system configured for generating an optimal deployed configuration of a device inside a hollow structure of a subject for surgical planification, said method comprising:

• based on at least one image dataset representing at least one portion of the hollow structure of reference subjects:
• calculating a signed distance field of the at least one portion of the hollow structure for each among the reference subjects;
• and
• calculating at least one geometrical parameter of the at least one portion of the hollow structure for each among the reference subjects, wherein the at least one geometrical parameter comprises a set of geometrical parameters defining a geometrical model of a portion of the hollow structure of a reference subject among the reference subjects;
• for each portion among the at least one portion of the hollow structure of the reference subjects, generating a deployed in-use representation of the device by:
• computing contact forces between the device and said one portion of the hollow structure based on the corresponding calculated signed distance field;
• and
• applying said contact forces to a geometrical representation of the device;
• and
• training the machine learning system with each calculated geometrical parameter as an input variable and the corresponding deployed in-use representation of the device as an associated target output, so as to obtain a trained machine learning system configured to receive as an input at least one geometrical parameter and provide as output the deployed in-use representation of the device to be used in a method for generating an optimal deployed configuration of a device for surgical planification.

A device for training a machine learning system configured for generating an optimal deployed configuration of a device inside a hollow structure of a subject for surgical planification, the device comprising a processing unit configured to:

• based on at least one image dataset representing at least one at least one portion of the hollow structure of reference subjects:
• calculate a signed distance field of the at least one portion of the hollow structure for each among the reference subjects;
• and
• calculate at least one geometrical parameter of the at least one portion of the hollow structure for each among the reference subjects, wherein the at least one geometrical parameter comprises a set of geometrical parameters defining a geometrical model of a portion of the hollow structure of a reference subject among the reference subjects;
• for each portion among the at least one portion of the hollow structure of the reference subjects, generate a deployed in-use representation of the device by:
• compute contact forces between the device and said portion of the hollow structure based on the corresponding calculated signed distance field;
• and
• apply said contact forces to a geometrical representation of the device;
• and
• train the machine learning system with each calculated geometrical parameter as an input variable and the corresponding deployed in-use representation of the device as an associated target output, so as to obtain a trained machine learning system configured to receive as an input at least one geometrical parameter and provide as output the deployed in-use representation of the device to be used in a method for generating an optimal deployed configuration of a device for surgical planification.