Thyroid nuclear medicine image diagnosis method based on deep learning

An image diagnosis and deep learning technology, applied in neural learning methods, medical automation diagnosis, image enhancement and other directions, can solve the problems of low diagnosis efficiency and unsuitable hospital promotion, reduce inspection content, facilitate use and expansion, and improve robustness sexual effect

Inactive Publication Date: 2019-07-16
HARBIN UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

In the process of thyroid disease diagnosis, the essential texture details of the problem object determine the configuration of the network. The ro

Method used

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  • Thyroid nuclear medicine image diagnosis method based on deep learning
  • Thyroid nuclear medicine image diagnosis method based on deep learning
  • Thyroid nuclear medicine image diagnosis method based on deep learning

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Embodiment Construction

[0044] The present embodiment will be described in detail below with reference to the drawings.

[0045] A kind of thyroid nuclear medicine image diagnosis method based on deep learning of the present embodiment, such as figure 1 shown, including the following steps:

[0046] Step 1. The imaging instrument used is Siemens SPECT single-probe E.CAM method, equipped with a low-energy general-purpose collimator, with an energy peak of 140Kev, a window width of 20%, a matrix of 128*128, and a magnification of 3.2 times. The drug is freshly rinsed with 99mTcO4-185Mbg The labeling rate was 99%. Static anterior imaging was performed 20 minutes after intravenous injection, and 136 original medical image samples were collected. Considering the shape, size, position and function of the thyroid gland in nuclear medicine images, it was diagnosed. According to the diagnostic results Give medical image labels, the labels include four categories of hyperthyroidism, Hashimoto's disease, hypot...

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Abstract

The invention discloses a thyroid nuclear medicine image diagnosis method based on deep learning, and belongs to the technical field of information. The problem that thyroid nuclear medicine image diagnosis is limited to a certain extent and the diagnosis efficiency is low in the prior art is solved. The method comprises the following steps: determining an ROI area by using a three-channel threshold detection method, and cutting pictures; performing scaling, rotation and Gaussian noise addition processing on the focus image set to form an extended sample data set; establishing a convolutionalneural network for image diagnosis, and training the network by using the training set to obtain a trained novel diagnosis model; performing disease diagnosis on the test set by using the trained diagnosis model to obtain a thyroid disease diagnosis result of each medical image. According to the method, the trained model has higher robustness, and the popularization possibility of the model in hospitals is enhanced.

Description

technical field [0001] The present application belongs to an image diagnosis method, belongs to the field of information technology, and specifically relates to an image diagnosis method of thyroid nuclear medicine based on deep learning. Background technique [0002] The thyroid is a hormone-producing gland located in the lower front of the neck that controls the rate at which energy is used, makes protein, regulates the body's sensitivity to other hormones, regulates metabolism, growth rate, and regulates other bodily processes. In recent years, the incidence of thyroid cancer in various countries has gradually increased, and the global incidence is increasing at an annual rate of 6.2%. In China, the incidence of thyroid cancer is showing an obvious upward trend. The thyroid is the largest endocrine gland in the human body, and its disease detection rate is increasing day by day, and this trend is more obvious with the increase of age. At present, SPECT medical imaging is...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06K9/32G06K9/62G06N3/04G06N3/08G06T7/136G16H50/20
CPCG06T7/11G06T7/136G16H50/20G06N3/08G06T2207/30004G06V10/25G06N3/044G06N3/045G06F18/214
Inventor 刘跃军孟祥政徐义飞马立勇
Owner HARBIN UNIV OF SCI & TECH
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