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Automatic segmentation of brain tumor images based on convolution neural network

A convolutional neural network and automatic segmentation technology, applied in the field of medical image segmentation and deep learning, can solve the problems of long image preprocessing time, rough segmentation of brain tumor images, and unbalanced categories, and achieve shortened processing time and image preprocessing. Handle the effect of convenient operation and solve the category imbalance

Active Publication Date: 2018-12-18
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for automatic segmentation of brain tumor images based on convolutional neural networks, which solves the technical problems of rough segmentation of brain tumor images, unbalanced categories and long image preprocessing time in the prior art

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  • Automatic segmentation of brain tumor images based on convolution neural network
  • Automatic segmentation of brain tumor images based on convolution neural network
  • Automatic segmentation of brain tumor images based on convolution neural network

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Embodiment 1

[0037] The method for automatically segmenting brain tumor images based on convolutional neural networks in the present invention includes multimodal MRI images of brain tumors, and further includes the following steps:

[0038] Step 1. Acquire multimodal MRI images of brain tumors and perform image preprocessing to obtain an original image set;

[0039] Step 2. Construct a framework for brain tumor segmentation based on multimodal MRI images; the framework includes module one and module two, and module one includes 3D convolutional neural network, residual unit and transposed convolution as the basis to form a parallel The deep deconvolution neural network is used to output the contour map of the brain tumor segmentation image; the second module includes adding a skip structure on the basis of the deep deconvolution neural network structure in the module one, which is used to output the lesion region segmentation of the brain tumor image picture;

[0040] Step 3. Input the o...

Embodiment 2

[0048] This embodiment makes the following further limitations on the basis of Embodiment 1: in the step 1, the multimodal MRI images are four kinds of modality images Flair, T1, T2, T1C, and the two modes of Flair and T2 N4ITK is used to correct the bias field of the images in the different modalities, and adjust the contrast of the images in the T1C and T1 modalities. Grayscale normalization of images between different individuals: first subtract the mean value of the entire image and divide by the standard deviation of the brain region, adjust the pixel values ​​​​of all images to the [-5, 5] interval, and normalize the entire image Normalized to [0, 1], non-brain regions are set to 0. Finally, translation transformation, twist enhancement and elastic deformation are performed on the preprocessed data. The detailed process is as follows: firstly, multimodal MRI images of brain tumors need to be acquired. In this method, only four modal images of Flair, T1, T2, and T1C are ...

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Abstract

The invention discloses a brain tumor image automatic segmentation method based on a convolution neural network, which comprises the following steps: acquiring a multimodal MRI image of the brain tumor and carrying out image preprocessing to obtain an original image set; constructing the frame of brain tumor segmentation based on multimodal MRI images; the frame comprises a module 1 and a module 2. The module 1 comprises a parallel deep deconvolution neural network based on a 3d convolution neural network, a residual unit and a transposed convolution; the module 2 includes adding a hopping structure on the basis of the deep deconvolution neural network structure in the module 1; after several iterative training, the ideal weights are obtained and the segmentation images of brain tumors areoutput. Image segmentation results are tested and evaluated. The invention effectively solves the problem of low accuracy of brain tumor segmentation image segmentation. It can improve the recognition of tumors and make the image preprocessing more convenient. Using the loss function based on d coefficients in the segmentation module can effectively solve the problem of class imbalance.

Description

technical field [0001] The invention relates to the fields of medical image segmentation and deep learning, in particular to a method for automatic segmentation of brain tumor images based on convolutional neural networks. Background technique [0002] Medical image segmentation is a key technology in image analysis and processing. Separating relevant tissues of interest according to the similarity and specificity of the image area is of great significance to the clinical diagnosis and treatment process and is the main premise of all follow-up work. , the quality of the segmentation effect will directly affect the smooth progress of the information processing work. Medical image segmentation of brain tumors is an important branch in the field of image segmentation. Brain tumor segmentation technology plays an important role in the clinical diagnosis and treatment of brain tumors. Through the segmentation results of brain tumors, doctors can measure tumor Size and location, ...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/12G06N3/04
CPCG06T7/11G06T7/12G06T2207/30096G06T2207/30016G06T2207/20084G06T2207/20081G06T2207/10088G06N3/045
Inventor 程建郭桦苏炎洲高银星许轲
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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