# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 2 Related Work  
Medical image analysis is the process of analyzing the images to solve practical clinical issues. Medical imaging analysis has helped radiologists and doctors to make diagnostic and treatment planning process more accurate [15]. Some computer-aided diagnosis (CADx) system relied on practical medical analysis as a vital factor and can directly affect the operation of clinical examination and treatment [16; 17]. The purpose of medical imaging analysis is to extract useful features, which are helpful for improving clinical diagnosis. In CADx, machine learning has been playing a very important role in popular applications such as tumor segmentation, classification, nodule detection, image-guided therapies, medical image retrieval, and annotation [18; 19; 20]. In the past, traditional handcrafted features were used for machine learning. The task of selecting certain features and estimating them is complicated. Deep learning is commonly used for the purposes of medical image processing and analysis among the machine learning techniques because useful information and features are automatically learned by the neural network. Segmentation in the medical image is a requirement for medical application such as brain segmentation [21; 22], body organ segmentation [23; 24; 25], and cardiac ventricle segmentation [26]. The accurate segmentation results are useful in the diagnosis and treatment planning. The relevant information extracted in medical images using the segmentation methods includes shape, length, volume, relative organ location, and abnormalities [27]. An iterative 3D multiscale Otsu threshold method is introduced in [28] for the segmentation of the medical image. The impacts of noise and weak edges are reduced by multilevel image representation. A hybrid methodology for the automatic segmentation of ultrasound images is proposed in [29]. The proposed approach combines knowledge from Fuzzy clustering of spatial constraint-based kernels with the edge-based features of the regularized level set. An experiment was applied to test the method on both real images and synthetically generated images. Brain tumor segmentation has attracted much attention and many methods have been proposed. In the early works, brain tumor segmentation methods were often based on thresholds, edges, regions, atlases, classifications, and clustering methods [30]. These methods usually do not consume a lot of computational resources, but they do not achieve high performance and require user interaction. The multimodal brain tumor segmentation challenge (BRATS) was introduced for the first time in 2012. Many methods have been proposed for the task of segmenting brain tumors. [PERSON] et al. [31] proposed a method that compared the differences between the atlas of the normal healthy brain with the patient scan for brain tumor segmentation. One drawback of the method is that it excludes the tumor-induced mass effect caused by the deformation of adjacent healthy structures, which may cause incorrect registration. Some researchers propose a method, which uses tumor growth models to modify the healthy atlas and performs both registration and segmentation to a new brain atlas [32; 33]. These approaches can get the benefit of taking tumor characteristics into account. However, tumor growth models are followed by further complexities. [PERSON] et al. [34] have proposed a multi-atlas approach that relies on a searching algorithm for a similar patch of images. Numerous other active-contour approaches were proposed [35; 36]. All of these methods rely on the symmetry and/or alignment features of the left-right brain. Since it is a challenging task to align a brain with a large tumor onto a template, some methods complete the registration task and the tumor segmentation task at the same time. Some voxel-wise classifiers based on segmentation models such as random forests [37; 38] or SVM [39; 40] obtained promising results. For example, [PERSON] et al.