SP-FCN-based MRI brain tumor image segmentation system and method

An image segmentation and brain tumor technology, applied in the field of MRI brain tumor image segmentation system based on SP-FCN, can solve the problems of artifact interference, low segmentation accuracy, long training time, etc. easy-to-capture effects

Active Publication Date: 2020-11-13
SHANDONG NORMAL UNIV
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AI Technical Summary

Problems solved by technology

[0004] The inventors found that in the prior art, for the MRI brain tumor image segmentation problem, it is mainly realized by using deep learning algorithm, and the general processing flow is to pre-image the image Processing, extracting useful features, and finally training the model based on the extracted features to achieve segmentation; for MRI brain tumor image segmentation, commonly used methods include segmentation methods based on regions, thresholds, and edge detection. Region-based segmentation methods are more sensitive to noise , the threshold-based segmentation method is a difficulty in selecting the optimal threshold, and the segmentation method based on edge detection has low segmentation accuracy. In additi

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  • SP-FCN-based MRI brain tumor image segmentation system and method
  • SP-FCN-based MRI brain tumor image segmentation system and method

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Embodiment 1

[0036] The purpose of this embodiment is to provide an MRI brain tumor segmentation method based on SP-FCN.

[0037] The data set selected in this implementation case has a total of 400 samples, the total number of samples used for training is 300, and the total number of samples used for testing is 100, such as figure 1 As shown, the overall process of the MRI brain tumor segmentation algorithm based on SP-FCN is shown, an MRI brain tumor segmentation method based on SP-FCN, including:

[0038] Obtain MRI brain tumor images of different modalities of the same sample, and divide the brain tumor image area into image blocks of equal size;

[0039] performing superpixel calculation on the image block;

[0040] performing spectral clustering based on the superpixel results of the image block, and segmenting the superpixels of the image block to obtain tumor superpixels and non-tumor superpixels;

[0041] Identify the image block where the tumor superpixel is located in the orig...

Embodiment 2

[0100] The purpose of this embodiment is to provide an MRI brain tumor segmentation system based on SP-FCN.

[0101] An MRI brain tumor segmentation system based on SP-FCN, including:

[0102] The image acquisition module is used to acquire MRI brain tumor images of the same sample with different modalities by using a nuclear magnetic resonance instrument, and divide the brain tumor image area into image blocks of equal size;

[0103] A superpixel calculation module, configured to perform superpixel calculation on the image block;

[0104] The superpixel segmentation module is used to perform spectral clustering based on the superpixel results of the image block, segment the superpixels, and obtain tumor superpixels and non-tumor superpixels;

[0105] The tumor image recognition module is used to identify the image block where the tumor superpixel is located in the original MRI brain tumor image, and obtain ROI images of MRI brain tumor images with different modalities;

[0...

Embodiment 3

[0162] The purpose of this embodiment is to provide an electronic device.

[0163]An electronic device, comprising, a memory, a processor, and a computer program stored on the memory, and the processor implements the following steps when executing the program, including:

[0164] Obtain MRI brain tumor images of different modalities of the same sample, and divide the brain tumor image area into image blocks of equal size;

[0165] performing superpixel calculation on the image block;

[0166] performing spectral clustering based on the superpixel results of the image block, and segmenting the superpixels of the image block to obtain tumor superpixels and non-tumor superpixels;

[0167] Identify the image block where the tumor superpixel is located in the original MRI brain tumor image, and obtain the ROI of the MRI brain tumor image with different modalities;

[0168] The ROIs of different modalities were input into the FCN network model for image fusion to obtain the tumor ...

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Abstract

The invention provides an SP-FCN-based MRI brain tumor image segmentation system and method. The method comprises the steps of extracting features of brain tumor images in different modes through a superpixel method to obtain ROI segmentation results under different modals; taking ROI images under different modals as the input data of the FCN, carrying out data fusion to obtain a final segmentation result, and taking the ROI images under different modals as the input, so that the calculation amount of the data is reduced, and the final result is more accurate; meanwhile, the difference of white matter lesion images can be more easily captured by adopting multi-mode image fusion than a single-mode image, and the stability of the algorithm can be improved by multiple registration.

Description

technical field [0001] The disclosure belongs to the field of medical image processing and deep learning, and in particular relates to a SP-FCN-based MRI brain tumor image segmentation system and method. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Regarding the application of MRI brain tumor image segmentation methods, the development in the medical field is relatively mature. With the improvement of people's awareness of life safety and the wide application of medical image processing technology in the fields of pattern recognition, physics and computer, higher requirements have been put forward in terms of accuracy and speed of brain tumor image segmentation. Therefore, in order to achieve real-time and accurate segmentation of brain tumor images and meet the requirements of brain tumor image segmentation, it is necessary to establ...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06T7/11G06N3/08G06T2207/10088G06T2207/30096G06V10/25G06N3/045G06F18/23G06F18/25
Inventor 王晶晶张春慧杜勇涛于子舒栾振业
Owner SHANDONG NORMAL UNIV
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