Medical imaging conversion method and associated medical imaging 3D model personalization method

A medical imaging conversion method comprising:

• automatically converting:
• at least one or more real x-ray images of a patient, including at least a first anatomical structure of said patient and a second anatomical structure of said patient,
• both into at least a first digitally reconstructed radiograph (DRR) and a second digitally reconstructed radiograph (DRR) of said patient:
• said first digitally reconstructed radiograph (DRR) representing said first anatomical structure without representing said second anatomical structure,
• said second digitally reconstructed radiograph (DRR) representing said second anatomical structure without representing said first anatomical structure,
• by a single operation using either one convolutional neural network (CNN) or a group of convolutional neural networks (CNN) which is preliminarily trained to, both or simultaneously:
• differentiate said first anatomical structure from said second anatomical structure by isolating said anatomical structures directly in an original 3D volume, and
• convert a real x-ray image into at least two digitally reconstructed radiographs (DRR) by one of:
• (i) simultaneously producing a set of several DDRs, each of the DDRs focused on only one anatomical structure of interest, and
• (ii) producing one DDR which is only focused on one anatomical structure of interest excluding the other anatomical structures.

A medical imaging conversion method comprising:

• automatically converting:
• at least one or more images of a patient in a first domain, including at least a first anatomical structure of said patient and a second anatomical structure of said patient,
• into at least an image of said patient in a second domain, representing said first anatomical structure without representing said second anatomical structure,
• by a single operation using either one convolutional neural network (CNN) or a group of convolutional neural networks (CNN) which is preliminarily trained to, both or simultaneously:
• differentiate said first anatomical structure from said second anatomical structure by isolating said anatomical structures directly in an original 3D volume, and
• convert an image in a first domain into at least one image in a second domain by one of:
• (i) simultaneously producing a set of several DDRs, each of the DDRs focused on only one anatomical structure of interest, and
• (ii) producing one DDR which is only focused on one anatomical structure of interest excluding the other anatomical structures.

A medical imaging conversion method comprising:

• automatically converting:
• at least one or more global images of a patient in a first including at least several different anatomical structures of said patient,
• into several regional images of said patient in a second domain, respectively representing said different anatomical structures,
• by a single operation using either one convolutional neural network (CNN) or a group of convolutional neural networks (CNN) which is preliminarily trained to, both or simultaneously:
• differentiate said anatomical structures from one another by isolating said anatomical structures directly in an original 3D volume, and
• convert an image in a first domain into at least one image in a second domain by one of:
• (i) simultaneously producing a set of several DDRs, each of the DDRs focused on only one anatomical structure of interest, and
• (ii) producing one DDR which is only focused on one anatomical structure of interest excluding the other anatomical structures.