The invention discloses an MRI (
Magnetic Resonance Imaging)
brain tumor segmentation method based on a multi-scale attention mechanism and a secondary segmentation strategy, and belongs to the field of semantic segmentation. The method comprises the following steps: firstly, acquiring
MRI brain tumor data and preprocessing the
MRI brain tumor data; secondly, designing a 3D U-Net model, and combining a multi-scale attention mechanism and depth separable
convolution to extract features; then, secondary segmentation guided by a
cascade strategy is introduced; and finally, predicting by using the optimal model, storing and verifying a result, and calculating and comparing evaluation indexes. A depth separable
convolution module is used for replacing a traditional
convolution module, so that the parameter and calculation cost is reduced, and the segmentation precision is kept. In addition, a multi-scale attention fusion module is introduced to the first two
layers of the
encoder, the ability of capturing shallow information is enhanced, and important features are further extracted. A
cascade attention mechanism is introduced into a decoder, and the segmentation capability of a
small target area is improved. And training is carried out through a BraTS2020
data set, and the result shows that the optimized network significantly improves the
MRI brain tumor segmentation accuracy.