Brain image segmentation method based on deep learning
An image segmentation and deep learning technology, applied in neural learning methods, image analysis, image enhancement, etc., can solve problems such as blurred edges of segmented images, achieve high segmentation accuracy and efficiency, solve poor segmentation effects, and solve network training gradients Diffusion effect
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[0029]Example 1
[0030]Brain image segmentation method based on deep learning, including:
[0031]S1: Get the original brain image data set;
[0032]Get the brain MRI image data and the divided hippocampus label image data from Alzheimer's Disease NeuroImaging Initiative (ADNI) library, including real patients and health compare people, data formats are NIFTI.
[0033]S2: Preprocessing the obtained original brain image data set;
[0034]The image of the brain image data set is rotated, mirror, flip, and color jitter, crop size, adjustment image resolution, and divide the brain image data set into training set and test set according to the proportion of 8: 2.
[0035]S3: Training the preprocessed brain image data integration into the brain image segmentation model, dividing the brain image with the training mature brain image division model, and finally obtains the segmentation result;
[0036]Enter the pre-processed training set image data into the U-NET network model for training, to obtain the split ...
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