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Method for training brain image segmentation model and brain image segmentation method

A technology of segmentation model and brain image, applied in the field of image processing

Pending Publication Date: 2021-09-10
XUANWU HOSPITAL OF CAPITAL UNIV OF MEDICAL SCI +1
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Problems solved by technology

[0005] However, known neural network algorithms for brain image segmentation still have deficiencies, and further improving the efficiency of brain image segmentation methods has been a goal in this field

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  • Method for training brain image segmentation model and brain image segmentation method
  • Method for training brain image segmentation model and brain image segmentation method
  • Method for training brain image segmentation model and brain image segmentation method

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Embodiment Construction

[0025] The following description sets forth example embodiments in accordance with the present disclosure. Other example embodiments and implementations will be apparent to those of ordinary skill in the art. Furthermore, those of ordinary skill in the art will recognize that various equivalent techniques may be applied in place of or in combination with the embodiments discussed below, and all such equivalents are considered to be encompassed by this disclosure. of.

[0026] According to a first aspect of the present invention, a method for training a brain image segmentation model is provided, comprising the following steps:

[0027] (a) obtaining a brain imaging data set;

[0028] (b) using multiple brain image analysis software to segment each brain image in the brain image data set to obtain brain image data including multiple sets of machine labels;

[0029] (c) manually labeling part of the brain images in the brain image data set, so that part of the brain image dat...

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Abstract

The invention relates to a method for training a brain image segmentation model. The method comprises the following steps: collecting a brain image data set; segmenting the brain image by using a plurality of analysis software to obtain a plurality of groups of machine labels; performing manual labeling on a part of the brain image to obtain an artificial label; and based on the brain image data set and the associated label, iteratively training the segmentation model to be trained by circularly executing the following steps to obtain a target segmentation model: inputting the brain image into the segmentation model to be trained to obtain a predicted segmentation result; according to the consistency between the machine labels of the single voxels, determining the weights of the labels; calculating a loss function value according to the difference between the predicted segmentation result and each label and the weight; adjusting network parameters of the to-be-trained segmentation model based on loss function value minimization to obtain a current iteration segmentation model; wherein the weight of the machine label of the single voxel is negatively correlated with the consistency. The invention further relates to a brain image segmentation method and a brain image segmentation device.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for training a brain image segmentation model, a brain image segmentation method and a brain image segmentation device. Background technique [0002] Segmentation based on magnetic resonance brain images is a key step in the quantitative analysis of brain images. Researchers have developed many segmentation algorithms. The main algorithms can be divided into three categories: 1) methods based on image grayscale. Different brain tissues have different gray levels in MRI images, and the brain tissue can be segmented by using the difference in gray levels. Common methods include threshold segmentation, region growing, clustering, etc. 2) Method based on template matching. Using this type of method first requires obtaining one or more manually finely segmented brain image templates. The image to be segmented and the template are registered by rigid or non-rigid...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06N3/02G06K9/62
CPCG06T7/11G06N3/02G06T2207/30016G06F18/214
Inventor 齐志刚安彦虹李坤成刘倩
Owner XUANWU HOSPITAL OF CAPITAL UNIV OF MEDICAL SCI
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