Method and electronic device for classifying vessel

An electronic device comprising:

• at least one processor;
• and
• a memory storing computer-readable instructions that, when executed by the at least one processor, are configured to cause the electronic device to:
• acquire an image including cardiovascular vessels of a subject and angle information of an image acquisition device for the subject;
• identify at least one type of a cardiovascular vessel included in the image by:
• providing, as input to a trained machine learning model, the image and the angle information;
• and
• receiving, as output from the trained machine learning model, output data indicating the at least one type of the cardiovascular vessel included in the image;
• output a signal associated with the image, wherein the signal indicates the identified at least one type of the cardiovascular vessel;
• identify one or more errors in the output data indicating the at least one type of the cardiovascular vessel included in the image;
• and
• correct the one or more errors by further training the trained machine learning model,
• wherein the trained machine learning model is generated by training, using training data, an artificial neural network to receive an input image and input angle information and output an identification of one or more types of cardiovascular vessels depicted in the input image, wherein training the artificial neural network comprises modifying one or more weights of one or more nodes of the artificial neural network based on the training data, and
• wherein the training data comprises:
• a plurality of images of one or more cardiovascular vessels at a variety of different angles;
• information indicating, for each of the plurality of images, one or more angles;
• and
• tagging information indicating, for each cardiovascular vessel of the one or more cardiovascular vessels, a type of the cardiovascular vessel.

A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by at least one processor, cause the at least one processor to:

• acquire an image including cardiovascular vessels of a subject and angle information of an image acquisition device for the subject;
• identify at least one type of a cardiovascular vessel included in the image by:
• providing, as input to a trained machine learning model, the image and the angle information;
• and
• receiving, as output from the trained machine learning model, output data indicating the at least one type of the cardiovascular vessel included in the image;
• and
• output a signal associated with the image, wherein the signal indicates the identified at least one type of the cardiovascular vessel;
• identify one or more errors in the output data indicating the at least one type of the cardiovascular vessel included in the image;
• and
• correct the one or more errors by further training the trained machine learning model,
• wherein the trained machine learning model is generated by training, using training data, an artificial neural network to receive an input image and input angle information and output an identification of one or more types of cardiovascular vessels depicted in the input image, wherein training the artificial neural network comprises modifying one or more weights of one or more nodes of the artificial neural network based on the training data, and
• wherein the training data comprises:
• a plurality of images of one or more cardiovascular vessels at a variety of different angles;
• information indicating, for each of the plurality of images, one or more angles;
• and
• tagging information indicating, for each cardiovascular vessel of the one or more cardiovascular vessels, a type of the cardiovascular vessel.