Brain tumour image segmentation method and device based on improved full convolutional neural network

Abstract

The invention relates to the medical apparatus field, aims to propose an improved full convolutional neural network to realize machine segmentation of a brain tumor nuclear magnetic resonance image, avoids low efficient and instability existing in manual segmentation, provides the rapid and reliable brain tumor segmentation result and provides accurate bases for diagnosis, treatment and operationof brain tumors. The method comprises steps that 1), an image is selected; 2), a full convolutional neural network FCN model is constructed; 3), the segmentation result is tested, when the FCN model is trained, the trained model is utilized to predict the tumor position and the boundary size of any brain tumor image, corresponding evaluation indexes are utilized to evaluate the segmentation result, and the FCN model can be better ameliorated. The method is advantaged in that the method is mainly applied to NMR image processing occasions.

Description

technical field [0001] The present invention relates to medical devices, in particular to a method and device for segmenting brain tumor pictures based on an improved full convolutional neural network. Background technique [0002] The brain is the most important part of the human body, but the incidence of brain tumors has been on the rise in recent years. According to surveys, the number of brain tumors diagnosed in the United States increased by 23,000 in 2015 alone. The World Health Organization divides brain tumors into five grades according to the degree of lesions. Brain tumors are mainly divided into two types: benign tumors and malignant tumors. Benign tumors such as meningioma can generally recover after surgical treatment, while gliomas and gelatinous tumors can generally recover. Malignant tumors such as mesenchymal tumors are difficult to cure because of their stubbornness, so they are also called brain cancer. Magnetic resonance imaging (Magnetic Resonance Ima...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the deficiencies of the prior art, the present invention aims to propose an improved fully convolutional neural network to realize the machine segmentation of brain tumor MRI images, avoid the defects of low efficiency and instability in manual segmentation, and provide fast and reliable The segmentation results of brain tumors can provide accurate basis for the diagnosis, treatment and surgical guidance of brain tumors.

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