Generating reformatted views of a three-dimensional anatomy scan using deep-learning estimated scan prescription masks

A method comprising:

• employing, by a system operatively coupled to a processor, a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient;
• and
• reformatting, by the system, three-dimensional image data captured of the anatomical region of the patient using the masks to generate different representations of the three-dimensional image data that correspond to the different anatomical landmarks, wherein the different representations comprise at least one of synthetic two-dimensional images generated from the three-dimensional image data or synthetic three-dimensional images generated from the three-dimensional image data.

A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• employing a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient;
• and
• reformatting three-dimensional image data captured of the anatomical region of the patient using the masks to generate different representations of the three-dimensional image data that correspond to the different anatomical landmarks, wherein the different representations comprise at least one of synthetic two-dimensional images generated from the three-dimensional image data or synthetic three-dimensional images generated from the three-dimensional image data.