# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 2 Related Work  
In a two-dimensional U-Net network, two-dimensional convolutional kernels are used to acquire feature maps by sliding convolutional kernels from left to right and top to bottom on brain tumor MRI images [13]. MRI images are three-dimensional images, and even when multiple adjacent slices are used in a two-dimensional U-Net network, some of the stereoscopic information of the brain tumor can be preserved, but a significant amount of spatial information features will be lost. Therefore, considering a single MRI slice alone will have a certain number of false negatives in the segmentation results. A so-called false negative is an area, which is a tumor, and is predicted to be a non-tumor area. In contrast, the 3D convolutional neural network uses 3D convolutional kernels to add a front-to-back sliding direction in addition to left-to-right and top-to-bottom sliding on brain tumor MRI images to be able to extract brain tumor features, which can be complete. [PERSON] et al. [14] proposed a 2D-3D hybrid model to further improve the segmentation accuracy of tumors, but the network is complex and does not make sufficient use of tumor features. [PERSON] et al. [15] proposed a 2D deep residual U-Net with 104 convolutional layers, replacing the convolutional blocks of each layer in the U-Net encoder with stacked \"bottleneck\" residual blocks, and it achieved good segmentation results in the 2D brain tumor segmentation task in 2020. [PERSON] et al. [16] proposed a novel segmentation network, which can segment brain tumor subregions in MRI images more accurately by fusing multiscale features with complementary information in the encoder while employing an additive attention mechanism to reduce the semantic feature gap between the encoder and decoder during jump connection, but it still suffers from the relatively low segmentation accuracy of the tumor core and enhanced tumor compared to the whole tumor. [PERSON] et al. [17] designed a stacked residual block based on the original 3D U-Net network to address issues such as difficulty in training and slow convergence of the 3D U-Net network. While retaining more image features, it avoids the problem of deep network's inability to converge. At the same time, it replaces the traditional dice loss function with a mixed loss function, which increases the contribution of brain tumor pixel regions to the total loss. The experimental results show that the accuracy of this algorithm has been improved to a certain extent. In 2017, [PERSON] et al. [18] were inspired by the U-Net network model and proposed a segmentation algorithm with a structure similar to the U-Net. They added a dropout layer between the convolutional layers, using  
Figure 1: Brain tumor images in different modes. 3 \(\times\) 3 \(\times\) 3 convolutional layers to replace the maximum pooling layer while using data augmentation technology to obtain more training data to prevent overfitting. [PERSON] et al. [19] proposed an S3D-UNet structure for automatic segmentation of brain tumors. Adopting separable 3D convolutions instead of conventional convolutions solves the problems of high computational cost and high memory configuration in 3D convolutions. Not only does it reduce computational complexity, but it also fully utilizes the information of 3D MRI images. Experiments have shown that using separable 3D convolutions reduces computational costs, and the algorithm has achieved good results. [PERSON] et al. [20] proposed a segmentation network for 3D MRI brain tumor images based on an encoding-decoding structure. A variational automatic encoder branch (VAE branch) was added at the end node of the encoder. Adding VAE branches can add additional guidance and normalization to the encoder output section, reconstruct the original input image, and optimize feature clustering results. This not only improves the performance of segmentation but also consistently achieves good training accuracy during any random initialization.