Interactive coronary labeling using interventional x-ray images and deep learning

A system for generating a model to classify a vasculature, the classification system comprising:

• a processor in communication with memory, the processor configured to:
• obtain a trained model of the vasculature, wherein the trained model was trained from image data that represents a plurality of first vessel trees of a plurality of vasculatures and includes labeling of at least one vessel in each of the plurality of first vessel trees;
• obtain an image representing a second vessel tree;
• identify a deviation between a vessel geometry in the trained model and a vessel geometry in the second vessel tree;
• provide a labeling of the deviation;
• and
• adjust, based on the deviation and the labeling of the deviation, the trained model to classify the vasculature.

A non-transitory computer-readable medium having stored a computer program comprising instructions which, when executed by a processor, cause the processor to:

• obtain a trained model of a vasculature, wherein the trained model was trained from image data that represents a plurality of first vessel trees of a plurality of vasculatures and includes labeling of at least one vessel in each of the plurality of first vessel trees;
• obtain an image representing a second vessel tree;
• identify a deviation between a vessel geometry in the trained model and a vessel geometry in the second vessel tree;
• provide a labeling of the deviation;
• and
• adjust, based on the deviation and the labeling of the deviation, the trained model to classify the vasculature.