System and method for imaging

A system for training a neural network, comprising:

• a processor operable to execute instructions to:
• acquire a first image data of a subject taken with an imager having a first imaging modality;
• generate a first simulated model image to simulate an image of a model of a member taken with the first imaging modality based on a simulation of a projection of x-rays through the model using a predetermined interaction of the x-rays with one or more materials of the member;
• form an overlayed training image by overlaying the generated first simulated model image on the acquired first image at a position;
• label the overlayed training image;
• receive information associated with a grade of the overlayed training image,
• wherein the grade indicates a value that identifies whether the position of the first simulated model image on the acquired first image is likely to provide a positive result;
• train the neural network with a data set that includes the overlayed training image, information associated with the label, and the information associated with the grade;
• and
• a memory system to store the trained neural network.

A method of training a neural network, comprising:

• creating a training data set comprising:
• acquiring a first image of a subject with a first imaging modality;
• accessing a model of a member having at least a geometry and a material of the member included within the model;
• generating a first simulated model image that simulates an image of the member acquired with the first imaging modality based at least on the accessed model of the member based on simulating a projection of x-rays through the accessed model using a predetermined interaction of the x-rays with one or more materials of the member;
• forming a first overlayed training image by overlaying the generated first simulated model image on the acquired first image at a first position;
• labeling the first overlayed training image;
• and
• grading the first overlayed training image,
• wherein the grading indicates a value that identifies whether the first position of the first simulated model image on the acquired first image is likely to provide a positive result;
• accessing the created training data set with a processor to train the neural network with the first overlayed training image, with information associated with the labeling, and with information associated with the grading, at least by determining weights for a neuron within the neural network;
• and
• saving the trained neural network for accessing to segment a procedure image.