Full-connection CRF cascaded FCN and K mean value brain tumor segmentation algorithm

Abstract

The invention relates to a brain tumor segmentation algorithm based on combination of deep learning and a traditional segmentation algorithm, in particular to a full-connection CRF cascaded FCN and Kmean value brain tumor segmentation algorithm. The DenseCRF matches all the pixels in the original image with each pixel in the segmentation result of the FCN algorithm, finds the pixels with the sameattribute, supplementarily smoothes the input, improves the detail information of the segmentation result, and improves the segmentation precision. Meanwhile, in different segmentation algorithms, the segmentation standards are different, and the segmentation results obtained based on the algorithms of the different segmentation standards are complementary to each other by fusing the deep learning algorithm FCN with the different segmentation standards and the traditional segmentation algorithm K-means clustering, so that the segmentation results are closer to real segmentation images. Therefore, the brain tumor nuclear magnetic resonance image is segmented more accurately, and a more accurate tumor image is provided for high-precision repetitive measurement and evaluation of the brain tumor nuclear magnetic resonance image.

Description

technical field [0001] The present invention relates to a brain tumor segmentation algorithm based on the combination of deep learning and traditional segmentation algorithms, especially a cascaded fully convolutional neural network ( Fully Convolution Neural Network, FCN) and K-means clustering algorithm model fusion brain tumor segmentation algorithm, can be used to segment brain tumor MRI images more accurately, and provide more accurate and repeatable measurement and evaluation of brain tumor MRI images. Accurate tumor images. Background technique [0002] In order to use neuroimaging to evaluate the manifestations of brain tumors and the effectiveness of treatment before and after treatment, it is inevitable to perform high-precision repeatable measurement and evaluation of the lesion area, so the precise segmentation of medical images is a necessary step for measurement and evaluation. However, the structure of tumors appears in diseases with different sizes, extensio...

AI Technical Summary

Problems solved by technology

[0003] With the rapid development of computer hardware, in the medical field, computer-aided medical diagnosis methods have become an important research field in medical imaging, diagnostic radiation, and computer science. Among them, deep learning, which is better than traditional algorithms, has entered the medical field. , has achieved many excellent results, but the tumor data set has more complex irregular shape features than most natural image data sets, and the network characteristic of FCN is that it can obtain detailed bottom-level information, and the collection of upper-level information does not Not ideal, so the processing of edge details is rough and lacks optimization

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