A three-dimensional segmentation method of brain MRI hippocampus based on deep learning
A technology of deep learning and hippocampus, applied in the field of machine learning and computer vision, can solve problems such as low contrast, flocking of patients, uneven level of doctors, etc., and achieve fast computing speed, high segmentation accuracy and strong scalability Effect
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[0046] Specific embodiments of the present invention will be described in detail below in combination with technical solutions and accompanying drawings.
[0047] like figure 2 As shown, the network structure of the present invention mainly combines FCN, U-Net 3D and convolutional neural network CNN.
[0048] U-Net is a semantic segmentation network based on FCN. In the U-Net structure, down-sampling and up-sampling are combined, and bottom-level information is combined with high-level information. The bottom-level features (same resolution cascade) are used to improve the lack of up-sampling information. Significantly improve the accuracy of segmentation. However, medical image data is generally less, and the underlying features are still important. Compared with ordinary images, medical images have very high complexity, large gray scale range, and unclear boundaries, so the U-Net structure is very suitable. U-Net technology is used for medical image segmentation, such as ...
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