Automatic segmentation of brain tumor images based on convolution neural network
A convolutional neural network and automatic segmentation technology, applied in the field of medical image segmentation and deep learning, can solve the problems of long image preprocessing time, rough segmentation of brain tumor images, and unbalanced categories, and achieve shortened processing time and image preprocessing. Handle the effect of convenient operation and solve the category imbalance
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[0036] Example 1
[0037] The method for automatically segmenting brain tumor images based on convolutional neural network of the present invention includes the multi-modal MRI image of brain tumors, and further includes the following steps:
[0038] Step 1. Collect multi-modal MRI images of brain tumors and perform image preprocessing to obtain the original image set;
[0039] Step 2. Construct a framework for brain tumor segmentation based on multi-modal MRI images; the framework includes module one and module two, and module one includes a 3D convolutional neural network, residual unit and transposed convolution as the basis to form a parallel The deep deconvolution neural network is used to output the contour map of the brain tumor segmentation image; the second module includes a jump structure added to the deep deconvolution neural network structure in the module one, and it is used to output the lesion area segmentation of the brain tumor image Figure
[0040] Step 3. Input the...
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[0047] Example 2
[0048] This embodiment further defines the following on the basis of embodiment 1: In step 1, the multimodal MRI image is four modal images Flair, T1, T2, T1C, and the two modalities Flair and T2 The image in the state is corrected by N4ITK, and the contrast of the image in the T1C and T1 modes is adjusted. Standardize the gray level of images between different individuals: first subtract the average value of the entire image and divide by the standard deviation of the brain area, adjust the pixel values of all images to the interval [-5, 5], and return the entire image to Change to [0,1], and set the non-brain area to 0. Finally, translation transformation, distortion enhancement and elastic deformation are performed on the preprocessed data. The detailed process is as follows: First, it is necessary to acquire multi-modal MRI images of brain tumors. In this method, only four modal images of Flair, T1, T2, and T1C are used. Flair modal MRI images contain t...
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