## 2 Related Work  
### Deep Learning-Based Methods for Medical Image Segmentation  
Recently, deep learning has distinguished itself in the field of medical image segmentation, effectively promoting the rapid development of medical diagnoses. The emergence of convolutional neural networks (CNNs) [10] has shown many researchers the powerful processing capabilities of deep learning and has achieved great success in the fields of image classification, object detection, and speech recognition. However, in order to ensure its efficiency and number of parameters, the size of the input network must only be fixed, which limits the further improvement of the results. The rise of fully convolutional networks (FCNs) [11] benefits from the fact that images of any size can be input into the network, which has also piqued the interest of many medical image processing researchers. Although FCN can also be applied in the field of medical image segmentation, its performance is not as good as UNet, which makes UNet one of the most popular architectures used in medical image networks today [12; 13; 14; 15]. In UNet, the encoding branch is composed of convolution, normalization, activation function, and max pooling, while in the decoder part, it involves deconvolution, convolution, normalization, and activation functions. In the encoder section, as the network deepens, features are gradually extracted, and in the decoder section, detailed information of the image is restored. In order to fully utilize depth data in some medical images, the 3D UNet network was proposed and has achieved good performance in the field of medical images. Sequentially, many variants based on UNet and 3D UNet have been developed by researchers and have contributed to deep learning in the field of medical image segmentation. [PERSON] et al. [16] proposed DMFNet based on UNet, where the correlation between adjacent slices is captured by 3D convolutions, and the 3D multi-fiber unit can be leveraged to significantly reduce the computational cost. [PERSON] et al. [17] designed a model, which is a hybrid of the deep residual network and UNet, called dResU-Net. This model handles the issue of vanishing gradient and uses low-level and high-level features simultaneously for prediction making. Low-level features at each level are preserved by shortcut connections in this network. [PERSON] et al. [18] proposed MS UNet to further improve the final segmentation accuracy, using a multi-scale strategy, and this showed that the multi-scale features are meaningful in brain tumor segmentation. [PERSON] et al. [19] developed a Res-Gated-3 DUNet network, which combines the advantages of UNet, ResNet and signal gating to improve accuracy in glioma image segmentation results. [PERSON] et al. [20] designed an automatic brain tumor segmentation approach based on a multi-encoder. The inception module and dilated convolutions were utilized to extract multi-scale features from the images via the basic convolutional layer. A SEDNet based on UNet was exploited by [PERSON] [21], with a shallow encoder and decoder. Sufficient hierarchical convolutional blocks in the encoder part can learn the intrinsic features of brain tumors in brain slices, and the tiny local-level spatial features alongside the global-level features can be captured by a decoder with a selective skip path. To solve the problem of insufficient receptive fields and excessive computing costs, the 3D-EffUNet was proposed by [PERSON] et al. [22]. Owing to the superior performance of UNet in medical image segmentation, we chose UNet as our baseline framework.