Method and system for identifying pathological changes in follow-up medical images

A method for training a machine learned network including a neural network to identify pathological changes in follow-up medical images, the method comprising:

• providing training reference image data showing a body part of a patient;
• generating simulated follow-up image data by deforming the training reference image data using one or more models for biomechanical soft tissue deformation;
• calculating one or more deformation fields for the training reference image data and the simulated follow-up image data using at least one image registration, the one or more deformation fields describing anatomical deformations in the body part between the training reference image data and the simulated follow-up image data;
• aligning the training reference image data and the simulated follow-up image data using the one or more deformation fields to generate co-aligned image data;
• analyzing the co-aligned image data to identify pathological changes using the machine learned network, the pathological changes occurring in the body part from the training reference image data to the simulated follow-up image data;
• comparing a result of the analyzing with a known input based on the one or more models for biomechanical soft tissue deformation;
• and
• adjusting weights in the neural network based on the comparing.

A system for identifying pathological changes in follow-up medical images, the system comprising:

• an interface unit configured to receive reference image data showing a body part of a patient at a first time, and to receive follow-up image data showing the body part of the patient at a second time, the second time being after the first time;
• and
• at least one processor configured to execute computer-readable instructions to
• generate one or more deformation fields for the reference image data and the follow-up image data using at least one image registration, the one or more deformation fields describing anatomical deformations in the body part between the reference image data and the follow-up image data,
• align the reference image data and the follow-up image data using the one or more deformation fields to generate co-aligned image data, and
• analyze the co-aligned image data to identify pathological changes using a machine learned network, the pathological changes occurring in the body part of the patient between the first time and the second time, and the machine learned network being trained to identify pathological changes in the co-aligned image data.