Brain tumor segmentation method based on deep neural network and multi-modal MRI image

Abstract

The invention discloses a brain tumor segmentation method based on a deep neural network and a multi-modal MRI image. The method includes steps: constructing the deep neural network, wherein the deep convolution neural network includes two three-layer convolution layers, a three-layer full connection, and a classification layer, an input layer corresponds to the multi-modal MRI image, and each node of an output layer corresponds to a tumor classification label; performing MRI image preprocessing; training a network model; and testing the model, performing normalization on a to-be-segmented tumor image sequence by employing image blocks of an MRI image sequence and mean values and standard deviations thereof in a training process, inputting the normalized image sequence to the deep neural network with the optimization network connection weight, obtaining node values of the classification layer, and obtaining the tumor classification of a to-be-segmented brain tumor image. According to the method, tumor abstract topological characteristic information in the multi-modal MRI image is mined and extracted by employing the deep neural network, and high segmentation accuracy and high segmentation precision can be guaranteed in brain tumor segmentation of the multi-modal MRI images.

Description

technical field [0001] The invention relates to the fields of medical image segmentation, pattern recognition and machine learning, in particular to a brain tumor segmentation method based on a deep neural network and multimodal MRI images. Background technique [0002] Today, with the vigorous development of image processing technology, pattern recognition, and machine learning theories and methods, medical image processing, as one of the fields most closely related to human life, has attracted more and more attention following the footsteps of artificial intelligence. As an important branch of image segmentation, medical image segmentation of brain tumors is of great significance in computer-aided diagnosis of tumors, especially for 3D visualization, tissue quantitative analysis and surgical planning. With the development of MRI (Magnetic Resonance Imaging) imaging technology, it has become a diagnostic trend to obtain corresponding multimodal MRI images for a single patie...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a brain tumor segmentation method based on deep learning and multi-mode MRI images, which solves the technical problems of too rough tumor image segmentation and redundant image information in the prior art.

Images