Method for Parkinson's disease classification and lesion area marking of MRI image

A Parkinson's disease and image technology, which is applied in the field of computer vision medical images, can solve problems such as poor classification results and inaccurate lesion areas, and achieve the effects of reducing dimensions, improving training efficiency, and reducing the amount of calculation

Active Publication Date: 2021-08-17
SOUTHWEST JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

Therefore, using the existing model to classify and label MRI images, the classification results obtained are poor, and the labeled lesion area is inaccurate

Method used

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  • Method for Parkinson's disease classification and lesion area marking of MRI image
  • Method for Parkinson's disease classification and lesion area marking of MRI image
  • Method for Parkinson's disease classification and lesion area marking of MRI image

Examples

Experimental program
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Embodiment Construction

[0044] Specific examples of the present invention are given below to further illustrate the present invention.

[0045] S1: Download the medical image dataset, extract 1557 PD (Parkinson's disease) images, 543 Control (normal) images, and 193 Prodromal (latency) images according to the label documents.

[0046] S2: Preprocess the MRI images. For images that do not meet the resolution, use the cubic interpolation method and average pooling to perform upsampling and downsampling respectively, and then obtain the training set and save it in tfrecord format.

[0047] Downsampling uses the average pooling method, that is, only averages the feature points in the neighborhood. The formula is:

[0048] alpha i ∈{0,1},

[0049]

[0050]

[0051] Upsampling uses cubic interpolation.

[0052] S3: Construct classification module, such as figure 1 shown. The input is a tensor of [224,224,3], through the first convolutional layer (conv), the convolution kernel size is 7*7, and th...

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Abstract

The invention discloses a method for Parkinson's disease classification and focus area marking of an MRI image. The method comprises the steps of constructing a Parkinson's disease classification model of the MRI image; constructing a Parkinson's disease focus labeling model; classifying the MRI image to be detected by using the Parkinson's disease classification model of the MRI image; and for the MRI image determined to be the Parkinson's disease or the MRI image in the incubation period of the Parkinson's disease, marking a focus area by using a Parkinson's disease focus marking model. Wherein the step of constructing the Parkinson's disease classification model of the MRI image comprises the steps of constructing a classification module, inputting a training set into the classification module, and updating network parameters through back propagation to obtain the MRI image classification model. The beneficial effects of the method are that the method of first classification and then labeling is used, the location of the focus area is more accurate and effective on the basis of improving the accuracy of the classification model, unnecessary training is avoided, the training efficiency is greatly improved, and the robustness of the model is stronger.

Description

technical field [0001] The invention relates to the field of medical images of computer vision, in particular to a method for classifying Parkinson's disease of MRI images and marking lesion regions. Background technique [0002] With the development of medical imaging technology, CT, MRI and other medical imaging technologies have become an important basis and means for the diagnosis of Parkinson's disease. However, in the existing technology, it is necessary to rely on experts to analyze and confirm medical images, which not only consumes a lot of labor and is inefficient, but also because the imaging machines of each hospital are different, the gap in hardware will make the image of the same part of the same person There will also be great differences, which will have a great impact on manual analysis. [0003] With the rapid development of computer vision, more and more people combine computer vision with medical diagnosis, but there are still many problems in applying ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/70G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/70G06N3/084G06T2207/10088G06V2201/03G06N3/045G06F18/24
Inventor 张晓博张哲浩李伟
Owner SOUTHWEST JIAOTONG UNIV
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