Abstract
In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.
Highlights
Glioma is the most common primary brain tumor which originates from glial cells
In order to make full use of the advantages of superpixels and improve the accuracy and robustness of Glioblastoma multiforme (GBM) clustering, this paper proposes a multimodal MRI-GBM clustering algorithm based on superpixels
Of the experiment, we first determine the value range of the original input layer neighborhood of multimodal 3D-convolutional neural networks (CNNs) through experiments; we use the method of adding multimodal 3D-CNNs features to realize the clustering of brain tumor MRI images and analyze the differences shown by different patients compared with the method that does not add the features of multimodal 3D CNNs one by one; we compare the method based on the features of multimodal 3D-CNNs and the method based on the features of multimodal 2D-CNNs
Summary
Glioma is the most common primary brain tumor which originates from glial cells. Because of its characteristic of infiltrating the surrounding tissues, it is difficult to be completely removed by surgery [1]. Radiotherapy occupies a core position in the treatment of brain tumors This conventional treatment method often leads to the most common side effect of glioma patients within two years, that is, radiation necrosis of glioma [4]. E recurrence and necrosis of glioma appear similar on conventional images, and it is difficult to distinguish them. It is very important to distinguish between recurrence and necrosis of glioma at an early stage because the treatment strategies of the two are completely different. The method to distinguish the recurrence and necrosis of glioma is usually a follow-up, biopsy, and surgical operation. Erefore, combining different MRI modal images can improve tumor discrimination and better reflect the degree of tumor invasion. Experiments show that this algorithm can achieve better clustering results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
