Method and device for generating coronary artery center line extraction model

Abstract

The invention provides a method and a device for generating coronary artery center line extraction model, and the method comprises the steps: carrying out the segmentation processing of a coronary artery sample image, and obtaining a coronary artery sample segmentation image; performing coronary artery center line marking on the coronary artery sample segmentation image to obtain a coronary artery marking image; obtaining an output image of a first generator in a coronary artery center line training model trained last time, inputting the coronary artery sample segmentation image and the output image into the coronary artery center line training model, obtaining a coronary artery center line prediction result image, and the coronary artery center line training model comprises a plurality of generators connected in sequence; inputting the coronary artery center line prediction result image and the corresponding coronary artery marking image into a discriminator, performing loss calculation according to the judgment result of the discriminator, and if the loss meets a preset loss threshold value, obtaining a coronary artery center line training model meeting the loss threshold value. The complexity of a coronary artery center line extraction model can be reduced.

Description

technical field [0001] The present invention relates to the technical field of medical image analysis, in particular to a method and device for generating a coronary centerline extraction model. Background technique [0002] In the field of medical image analysis, the correct extraction of coronary artery centerline is of great significance for accurate medical diagnosis. At present, coronary artery centerline extraction is generally performed based on the coronary artery centerline extraction model generated by deep neural network training, for example, using the three-dimensional convolutional neural network (CNN, Convolutional Neural Network) coronary artery centerline extraction model, by Ten thousand training samples can realize the automatic extraction of coronary artery centerline. However, for this coronary artery centerline extraction model, since the learning ability of CNN needs to be based on a large sample size, for coronary arteries with a small sample size, i...

AI Technical Summary

Problems solved by technology

However, for this coronary artery centerline extraction model, since the learning ability of CNN needs to be based on a large sample size, for coronary arteries with a small sample size, it is necessary to increase the model parameters to ensure better extraction accuracy, so that the constructed coronary artery centerline extraction model The complexity is high, and the time required for training is longer

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