Nuclear magnetic image brain gray matter nucleus segmentation method based on convolutional neural network
A convolutional neural network and brain technology, applied in the field of brain gray matter nuclei segmentation in nuclear magnetic imaging, can solve the problems of limited expression ability of Gaussian mixture model, limited expression ability, lack of global information, etc., to improve segmentation accuracy and generalization ability, The effect of improving the extraction ability and reducing the parameters of the network model
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[0037]The method of the present invention will be described in detail with reference to the drawings and embodiments.
[0038]A schematic diagram of the segmentation method of brain gray matter nuclei in MRI images based on convolutional neural network is as followsfigure 2 Shown. The general process of the method is as follows:
[0039]1) The number of samples in the training set, validation set, and test set are 20, 4, and 19 respectively, and each sample contains QSM, T1, and a label. First, the gray value of QSM is truncated to [-150,250]; the gray value of T1 image is truncated to [0,800]. Then, normalize the two channels to between 0 and 1. Data enhancement uses three-axis random flip and random rotation centered on the z-axis, and the random rotation angle is limited to (-30°, 30°). The probability of each data enhancement being effective is 0.5. For all geometric space transformations, trilinear interpolation is used for the data in the interpolation method, and nearest neighbor i...
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