Abstract
The incidence of glioma is increasing year by year, seriously endangering people's health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the disease. Accurate segmentation of brain glioma has positive significance in medicine. However, due to the strong variability of the size, shape, and location of glioma and the large differences between different cases, the recognition and segmentation of glioma images are very difficult. Traditional methods are time-consuming, labor-intensive, and inefficient, and single-modal MRI images cannot provide comprehensive information about gliomas. Therefore, it is necessary to synthesize multimodal MRI images to identify and segment glioma MRI images. This work is based on multimodal MRI images and based on deep learning technology to achieve automatic and efficient segmentation of gliomas. The main tasks are as follows. A deep learning model based on dense blocks of holes, 3D U-Net, is proposed. It can automatically segment multimodal MRI glioma images. U-Net network is often used in image segmentation and has good performance. However, due to the strong specificity of glioma, the U-Net model cannot effectively obtain more details. Therefore, the 3D U-Net model proposed in this paper can integrate hollow convolution and densely connected blocks. In addition, this paper also combines classification loss and cross-entropy loss as the loss function of the network to improve the problem of category imbalance in glioma image segmentation tasks. The algorithm proposed in this paper has been used to perform a lot of experiments on the BraTS2018 dataset, and the results prove that this model has good segmentation performance.
Highlights
Brain tumor is an abnormal cell group that grows in brain tissue. is abnormal growth of cells seriously endangers human health
According to the location of origin, brain tumors are divided into two categories: the first category is primary brain tumors that originate in the brain, and the second category is secondary brain tumors that originate from malignant tumors outside the brain, but it has a spreading route. e starting point is to start from other parts of the body such as the digestive tract, liver, or breast and invade into the skull
In the 3D U-Net segmentation network, additional context constraints are generated for each tumor region; second, under the obtained constraints, the attention mechanism is used to fuse multisequence Magnetic resonance imaging (MRI) to achieve the segmentation of three subtumor regions; 3D U-Net model combines and refines the above prediction results [19]
Summary
Brain tumor is an abnormal cell group that grows in brain tissue. is abnormal growth of cells seriously endangers human health. MRI is a noninvasive brain tumor imaging technology that is safe and harmless It can provide clinicians with accurate information and become one of the important imaging technologies for the diagnosis and treatment of brain tumor diseases. E glioma images based on multimodal MRI can better reflect gliomas’ [5] specific location and shape; clinical radiologists usually combine four different modal images to comprehensively analyze and identify the area of glioma. Is paper studies how to use computer technology to automatically segment gliomas from normal brain tissues based on the image features in multimodal MRI images, so as to provide doctors with a basis for diagnosis and treatment. MRI images of different modalities are used, fully combining the complementary advantages of different modal images to provide supplementary information for the analysis of different subregions of glioma, which can effectively improve the accuracy of segmentation
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