Thyroid nodule invasiveness prediction method based on target detection

A technology for thyroid nodule and target detection, which is applied in the field of image processing, can solve the problems of insufficient accuracy, difficulty in taking into account the context information of the nodule itself and glandular tissue, and the inability to realize an end-to-end fully automatic system, etc.

Active Publication Date: 2021-06-08
INNER MONGOLIA UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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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 invasiveness of thyroid nodules based on target detection, which can overcome the difficulty of taking into accoun

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  • Thyroid nodule invasiveness prediction method based on target detection
  • Thyroid nodule invasiveness prediction method based on target detection
  • Thyroid nodule invasiveness prediction method based on target detection

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Embodiment 1

[0067] Such as figure 1 As shown, this embodiment provides a method for predicting the aggressiveness of thyroid nodules based on target detection, which method includes the following steps S1-S6:

[0068] S1: The adaptive wavelet algorithm is used to preprocess the clinically obtained thyroid ultrasound images, remove image noise and retain the edge information of the images in the high-frequency domain, and obtain the original data set.

[0069] Aiming at the problems of poor image quality, severe speckle noise, fuzzy edges of nodules, discontinuous boundaries, low contrast, and concentrated edge information and severe noise in the high-frequency domain. In this embodiment, an adaptive wavelet algorithm is used to preprocess the image. Wavelet filtering is based on the wavelet change, transforming the signal in the spatial domain to the wavelet domain with time-frequency characteristics, and then using the wavelet coefficients mapped by the threshold to reduce noise, and th...

Embodiment 2

[0110] Such as Figure 11 As shown, this embodiment provides a system for predicting the invasiveness of thyroid nodules based on target detection. The system adopts the method for predicting invasiveness of thyroid nodules based on target detection as in Example 1 to realize the invasiveness of nodules in thyroid ultrasound images. Prediction of sexual conclusions, the system includes:

[0111] A preprocessing module, which is used to preprocess the thyroid ultrasound image obtained clinically, remove image noise and retain the edge information of the image in the high frequency domain, and obtain the original data set;

[0112] A positioning network module, which is used to accurately locate the thyroid nodules in the images of the original data set, and then obtain a new image data set containing only nodules; simultaneously calculate and extract the aspect ratio information of the nodules and the context information of the target; and

[0113] A classification network mod...

Embodiment 3

[0115] This embodiment provides a terminal for predicting the aggressiveness of thyroid nodules based on target detection, which includes a memory, a processor, and a computer program stored on the memory and operable on the processor. The processor executes the target-based Detection method for predicting aggressiveness of thyroid nodules.

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Abstract

The invention belongs to the technical field of image processing, and particularly relates to a thyroid nodule invasiveness prediction method based on target detection. The method comprises the following steps: S1, preprocessing a thyroid ultrasound image obtained clinically to obtain an original data set; S2, constructing a positioning network model of a network structure based on a traditional Faster RCNN, and pre-training the positioning network model; S3, extracting nodule form information in the ultrasonic image by using a positioning network, and obtaining aspect ratio information of the nodules and context information of gland tissues; S4, constructing a classification network model; S5, establishing a multi-model fusion thyroid nodule invasiveness prediction network, and predicting the thyroid nodule invasiveness in the ultrasonic image; and S6, training and updating the classification network model in the fused network model, and storing the model with the highest accuracy in the verification set. According to the method, end-to-end full-automatic auxiliary diagnosis can be achieved, and the defects that a traditional method is insufficient in accuracy and low in detection rate 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 target detection. 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 efficiency of images; Prel...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/73G06T5/00G16H30/20G16H50/20G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/73G06T5/002G16H30/20G16H50/20G06N3/08G06T2207/10132G06T2207/20192G06T2207/20016G06T2207/30096G06T2207/20064G06N3/045G06F18/24
Inventor 郑志强陈家瑞翁智
Owner INNER MONGOLIA UNIVERSITY
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