Intelligent multi-scale medical image landmark detection

A method of machine learning for intelligent multi-scale image parsing, the method comprising:

• receiving, by a processor from a memory, a plurality of training images;
• training, by the processor based on the plurality of training images, an artificial agent to parse a test image to identify a landmark location in the test image, wherein training simultaneously trains:
• a search strategy model to search for the landmark location by parsing the test image by performing a series of actions, the series of actions comprises changing the position and the scale of a patch of the test image, wherein parsing the test image searches less than the entire test image;
• and
• an appearance model to identify the landmark location in the patch of the test image;
• and
• storing, in the memory, the trained artificial agent for automatically parsing a test image data to identify a landmark location in the test image.

A method for intelligent multi-scale landmark identification in an image, the method comprising:

• receiving image data representing the image;
• and
• automatically parsing, by a learned artificial agent comprising an optimal action-value function, the received image data to identify a landmark location in the image, wherein the learned artificial agent is configured to:
• parameterize a patch of the image data in a trained hierarchical data representation, the hierarchical data representation trained by maximizing a future reward of a reward system of the action-value function for each a plurality of available actions to reposition the patch of the image;
• determine a sequence of actions from the plurality of available actions to reposition and rescale the patch based on the parameterized patch of the image data;
• and
• identify the landmark location in the repositioned and rescaled patch of the image;
• and
• displaying the image, wherein the image comprises an annotation indicative of the identified landmark location.