# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 2 Related Work
[41] suggested a method that used information from neighboring voxel and far-off region, including the symmetrical section from the brain for classifying each voxel in magnetic resonance (MR) images. Recently, many researchers have applied convolution neural networks (CNNs) to segmenting brain tumors with MRI scans and have obtained very good results. The directions of the study can be divided into three categories. The first group used the 2D convolution in deep learning to make training faster but failed to learn 3D structures, which are one of the most important features in the medical image. The second group used 3D convolution to exploit the 3D features and they often got higher segmentation performance compared with 2D approaches but they require a high computational cost. The third group combined the 2D together with 3D to take advantage of the compactness of the 2D model and the volumetric information in 3D models. [PERSON] et al. [5] proposed U-Net, a fully convolution network with the encoder-decoder architecture. The U-Net is designed to train in an end-to-end manner to create a full resolution segmented mask. Despite this 2D-based approach method ignores the most important 3D features in the medical images, they have accomplished remarkable performance in brain tumor segmentation. [PERSON] et al. [42] upgraded the original U-Net to 3D U-Net by leveraging 3D operations such as 3D convolution layers and 3D max-pooling layers. [PERSON] et al. [43] proposed a network exploring the group convolution and dilated convolution that helps to not only reduce computing costs but also have good performance for segmentation of brain tumors. In brain tumors, the tumor substructures have a stratified structure. In particular, the enhanced tumor is found inside the tumor core and the tumor core is included in the whole tumor area. Many methods have utilized this property to increase the efficiency of brain tumor segmentation. [PERSON] et al. [44] and [PERSON] et al. [45] introduced a two-stages cascaded approach for brain tumor segmentation. At the first stage, the whole tumor was segmented and the tumor core and enhancing tumor were segmented in the second stage. [PERSON] et al. [46] presented a cascade of three CNN networks to segment whole tumor, tumor core and enhancing tumor, respectively. Although the above method achieved high efficiency, they had to build three almost identical models. It causes the redundancy of the model, complexity in training and ignoring the correlation among these models. In addition, training several models separately makes the quality of the latter models significantly affected by the former models. [PERSON] et al. [47] proposed an end-to-end network, called CU-Net, which exploited the hierarchical structure from brain tumors. However, the network was built based on 2D CNN and processed input MR images on every slice. It leads to the loss of three-dimensional space information, which is very important in medical imaging. [PERSON] et al. [43] utilized an existing parcellation to bring the brain's location information into patch-based neural networks that improve networks' brain tumor segmentation performance. [PERSON] et al. [48] proposed a partially reversible U-Net architecture. The reversible architecture can recover each layer's outputs from the subsequent layer's ones, eliminating the need to store activations for backpropagation. [PERSON] et al. [49] introduced an instantiation of the 3D U-Net [42] with minor modifications. They used patches of size \(128\times 128\times 128\) with a batch size of two for training, reducing the number of filters, reduced the number of filters right before upsampling, and used instance normalization instead of batch normalization for having more consistent results. [PERSON] et al. proposed 3D-ESPNet [50], which is extended from ESPNet, a fast and efficient network based on point-wise convolution for 2D semantic segmentation to 3D medical image data. [PERSON] et al.