Magnetic Resonance Image Segmentation Based on Multi-Scale Convolutional Neural Network

The incidence of nervous system and soft tissue is getting higher, and nuclear magnetic resonance is the preferred method of examination and is widely used. The segmentation of brain magnetic resonance (MR) images is the key to the subsequent operations such as three-dimensional reconstruction, quantitative analysis of normal tissues and diseased tissues. The accuracy of image segmentation is critical in the doctor's assessment with the location, shape, and size of the lesion tissue, as well as the determination of the disease and the correct diagnosis plan. The results of this study indicate that the multi-scale convolutional neural network (MSCNN) model can segment brain tumors accurately and effectively. Through multi-scale input, this paper overcomes the need to select specific input scales according to the size of the tumor, accommodate more neighborhood information from various angles, and adapt to different tumor sizes, thus improving the segmentation accuracy of brain tumors. Based on the same accuracy, the segmentation speed is accelerated to ensure the real-time segmentation further. This method can effectively segment the brain lesion tissue in the nuclear magnetic resonance image, which improves the generalization ability. It can be used for identifying the brain lesion tissue of the nuclear magnetic resonance medical image.