# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 1 Introduction  
Brain tumors are usually caused by the abnormal growth of a large quantity of cells in or around the brain and pose a considerable risk to human health [1; 2]. Gliomas, the most common brain tumors [3], are invasive [4] and classified into four levels by the World Health Organization (WHO) [5]. Low-grade gliomas (LGGs) with a grade of 1 or 2 and high-grade gliomas (HGGs) with a grade of 3 or 4 are generally associated with increased mortality [6; 7]. Medical imaging is often used in clinical diagnosis [8] to reduce brain tumor mortality. The medical images used for brain tumor diagnosis include X-rays [9], computed tomography (CT), and magnetic resonance imaging (MRI) [10]. Compared with other auxiliary methods, MRI is a primary tool for analyzing brain tumors [11; 12; 13] and has important clinical value in early diagnosis and treatment [14]. Conventional MRI in the BraTS2018 dataset involves fusing four sequences--T1-weighted (T1), T1 contrasted-enhanced (T1 ce), T2-weighted (T2), and fluid attenuated inversion recovery (FLAIR) sequences--to assess the brain structure [15]. A common problem is that it takes a long time for radiologists to segment medical images accurately, and the accuracy is highly dependent on the radiologist's experience [16; 17]. Additionally, segmentation by hand is subject to human error, and evaluation varies among radiologists [18], which accounts for the inconsistent results [19]. Many studies have proposed alternative segmentation methods for brain tumors. It is widely acknowledged that convolutional neural networks (CNNs) can be applied for imagesegmentation [20; 21; 22]. A CNN customizes feature prediction into a classification problem that can effectively capture image details [23]. The input image size must be fixed for a neural network with a well-designed parameter structure at all levels, such as AlexNet [24], VGGNet [25], and GoogleNet [26]. Fully convolutional network (FCN) is the first network applied to deep learning for semantic segmentation [27]. FCN enables a designed network to input images of any size [28]. Unlike FCN, which combines shallow features with deep features via addition operations, UNet combines shallow features with deep features by splicing. UNet, a network focused on medical image segmentation, has achieved excellent performance [29]. UNet has a U-symmetrical structure where the left branch performs the encoder task while the right branch conducts the decoder work. Another aspect of the architecture is that the encoder concatenates the corresponding layer of the decoder. UNet combines shallow features with deep features through a U-shaped network structure, which is used to perform the final semantic segmentation [30]. The blocks of UNet are generally composed of multiple convolutional layers [31]. A convolutional layer usually contains multiple feature maps with different weight vectors, which makes it possible to retain abundant image features. After the convolutional layer, the pooling layer is applied to the downsampling operation, which can reduce the size of the image and the number of parameters, thereby increasing the robustness of translation and deformation [32]. However, in the downsampling process, the features cannot be effectively extracted; therefore, important information and correlations between local and global features may be lost [33; 34]. The UNet bottleneck features can be decomposed in space or channels [35]. Spatial features produce the most important position information. In contrast, channel features focus on the semantic categories of the segmented objects [36]. An appropriate increase in the receptive field and global dependence benefits spatial feature extraction [37]. However, a problem is that the spatial features of the bottleneck block exploit only global dependencies or local features. The main contributions of our work are as follows:  
1. We proposed SARFNet, which can fully utilize the properties and characteristics of local and global features, to improve brain tumor segmentation performance. 2.