Magnetic resonance brain tissue segmentation method and device based on neural network, computing equipment and storage medium

Abstract

The invention discloses a neural network, a magnetic resonance brain tissue segmentation method and device based on the neural network, computing equipment and a storage medium. The neural network canbe a convolutional neural network, comprises at least one three-dimensional (3D) asymmetric convolution module or a convolutional neural network, and comprises at least one encoder, a bottleneck layer and a decoder which are coupled in sequence, and the at least one 3D asymmetric convolution module is located in at least one of the encoder, the bottleneck layer and the decoder; the 3D asymmetricconvolution module comprises a 3D standard convolution layer and three 3D asymmetric convolution layers corresponding to the 3D standard convolution layer, and the four formed convolution layers are used for carrying out convolution operation on data input into the 3D asymmetric convolution module, sum operation is carried out on convolution operation results and then the convolution operation results are output. By using the method, the brain tissue segmentation precision of the brain MRI image data can be improved.

Description

technical field [0001] The present application relates to the technical field of magnetic resonance brain image processing, in particular to a neural network, a neural network-based magnetic resonance brain tissue segmentation method, device, computing device and storage medium. Background technique [0002] Modern advanced multimodal neuroimaging techniques provide a new way of understanding the typical / atypical development of the human brain. With the availability of millimeter-scale infant brain imaging data and the rapid increase in the number of samples, researchers can map various rules of early human brain development from multiple perspectives (brain regions, brain connections and topological properties of brain networks), and provide insights into the development of human brains such as autism. The search for potential neuroimaging biomarkers in early brain diseases such as brain diseases offers great possibilities. In these studies, accurate brain tissue segmentat...

AI Technical Summary

Problems solved by technology

[0004] However, the current neural network-based infant brain segmentation model in the field is approaching the bottleneck period. In the famous iSeg infant brain tissue segmentation competition, various complex neural network connection layouts and functional module designs did not show much difference

At the same time, the design method focusing on the network connection mode may bring additional parameters and calculations, which does not match the performance improvement

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