A method for classifying a patient specific vasculature, the method comprising the steps of: a) providing an input unit with a training data set comprising diagnostic images of first vessel trees of a plurality of different vasculatures of patients with labels associated to at least the vessels of the first vessel trees that are considered most common in all the patients according to a standard anatomical model, wherein a label (LAD, LCX) identifies a vessel according to known medical terminology and coding standards and wherein said labels have been provided according to said terminology and coding standards; b) training a training device with an initial deep neural network model using as input the training data set, wherein the model derives from the training data set associations between particular vessels of said vasculatures and its corresponding labels, and wherein the model represents the different possible geometries of the vessels of the first vessel trees with its associated labels and; c) providing to the input unit at least one diagnostic image representing a second vessel tree of said patient specific vasculature; d) identifying by an inference unit at least one vessel related deviation between the representation by the initial model of the different possible geometries of the vessels of the first vessel trees and said second vessel tree, by comparison of the geometries of the vessels in the representation of first vessel trees by the initial model and of the vessels in the second vessel tree; e) in response to the identifying step, outputting an indication of said at least one deviation to a user, and providing at least one labeling for said at least one deviation according to said known medical terminology and coding standards, wherein the inference unit determines a potential labeling according to said known medical terminology and coding standards for said at least one deviation, suggests said potential labeling to the user and requests to the user a first user input indicating said at least one labeling, or the inference unit uses the indication to request the user to manually provide said at least one labeling; and f) changing or updating the initial model by retraining said training device with the initial model trained at step b), using as input to said model the at least one vessel related deviation identified at step d) and the at least one labeling for said deviation provided at step e), to classify the vasculature of said specific patient; a) providing an input unit with a training data set comprising diagnostic images of first vessel trees of a plurality of different vasculatures of patients with labels associated to at least the vessels of the first vessel trees that are considered most common in all the patients according to a standard anatomical model, wherein a label (LAD, LCX) identifies a vessel according to known medical terminology and coding standards and wherein said labels have been provided according to said terminology and coding standards; b) training a training device with an initial deep neural network model using as input the training data set, wherein the model derives from the training data set associations between particular vessels of said vasculatures and its corresponding labels, and wherein the model represents the different possible geometries of the vessels of the first vessel trees with its associated labels and; c) providing to the input unit at least one diagnostic image representing a second vessel tree of said patient specific vasculature; d) identifying by an inference unit at least one vessel related deviation between the representation by the initial model of the different possible geometries of the vessels of the first vessel trees and said second vessel tree, by comparison of the geometries of the vessels in the representation of first vessel trees by the initial model and of the vessels in the second vessel tree; e) in response to the identifying step, outputting an indication of said at least one deviation to a user, and providing at least one labeling for said at least one deviation according to said known medical terminology and coding standards, wherein the inference unit determines a potential labeling according to said known medical terminology and coding standards for said at least one deviation, suggests said potential labeling to the user and requests to the user a first user input indicating said at least one labeling, or the inference unit uses the indication to request the user to manually provide said at least one labeling; and f) changing or updating the initial model by retraining said training device with the initial model trained at step b), using as input to said model the at least one vessel related deviation identified at step d) and the at least one labeling for said deviation provided at step e), to classify the vasculature of said specific patient;