Thyroid nodule invasiveness prediction method based on deep learning segmentation network

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a thyroid nodule invasiveness prediction method based on a deep learning segmentation network. The method comprises the following steps: S1, preprocessing a thyroid ultrasound image obtained clinically; S2, constructing a main body structure framework based on a deep learning segmentation network; S3, improving the generative adversarial network model in the main body structure framework; S4, performing accurate semantic segmentation on the thyroid nodule, and counting information of nodule area, aspect ratio and contour rule degree; S5, obtaining a new image data set which only contains nodules after cutting; S6, improving the nonlinear expression ability of the classification network model; and S7, classifying prediction results by using the improved classification network model, and training and updating the classification network model. According to the method provided by the invention, end-to-end automatic auxiliary diagnosis can be realized, and the defects of insufficient accuracy and relatively low detection rate of a traditional detection method are overcome.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for predicting the aggressiveness of thyroid nodules based on a deep learning segmentation network. Background technique [0002] The thyroid is the largest endocrine gland in the human body. Ultrasound examination can make qualitative and quantitative estimates of its size, volume and blood flow, and can make qualitative or semi-qualitative diagnosis of benign and malignant tumors. Therefore, ultrasonic detection methods have also become imaging examinations. The preferred method for thyroid disease. In the past, the results of thyroid ultrasound examination were mainly judged by doctors based on experience, and conclusions were predicted. After the introduction of image recognition technology, various detection systems based on classification can replace manual processing and prediction of ultrasound image data, thereby greatly improving the detection efficie...

AI Technical Summary

Problems solved by technology

[0005] In view of the problems existing in the existing technical solutions, the purpose of the present invention is to provide a method for predicting the aggressiveness of thyroid nodules based on a deep learning segmentation network, which can overcome the defects of insufficient accuracy and low detection rate of traditional detection methods

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