Background Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. With the continuous development of deep learning, researchers have designed many automatic segmentation algorithms; however, there are still some problems: (1) The research of segmentation algorithm mostly stays on the 2D plane, this will reduce the accuracy of 3D image feature extraction to a certain extent. (2) MRI images have gray-scale offset fields that make it difficult to divide the contours accurately. Methods To meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise. Results We used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor, tumor core and enhanced tumor are 0.68, 0.85 and 0.70, respectively. Conclusion Although MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients.

Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.

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MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However, the manual segmentation of the MRI image is strenuous. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. Our model reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named “Categorical Dice,” and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results compared to the state‐of‐the‐art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhancing tumor.

Glioma is the most common primary tumor in the skull, but it has no obvious boundary with normal brain tissue and is difficult to completely remove. Currently, manual segmentation of the lesion regions has been widely used in the clinical practice of magnetic resonance (MR) images of gliomas, but the implementation process has disadvantages such as time‐consuming and poor repeatability. It is because of the shortcomings of traditional segmentation methods that we must seek other efficient technical means, which promote the development of automatic image segmentation technology. In this study, we propose a glioma automatic segmentation method called NLCA‐VNet. The framework is based on VNet, adding nonlocal and convolutional block attention modules, which can maintain more information, and can carry out attention in the channel and spatial dimensions, so that improve the segmentation effect. We employ the extended glioma MR image data set by the Brain Tumor Segmentation Challenge database (BraTS 2020, 2019, 2018), and finally obtained the effect image after tumor segmentation and achieved average Dice scores of 0.6702, 0.876, 0.7687, sensitivity of 0.7494, 0.9209, 0.7702, specificity of 0.0.9994, 0.9985, 0.9995, and Hausdorff95 of 50.8613, 9.3667, 12.4573 for enhancing tumor core, whole tumor, and tumor core in BraTS 2020, respectively. The results fully show that our method can fully adapt to the segmentation of glioma. To a certain extent, it improves the efficiency and accuracy of the doctor's diagnosis, which is of great significance to the scientific research and clinical aspects of glioma.