# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 1 Introduction  
With the rapid development of deep learning and computer vision technology, medical image segmentation has been widely applied in assisted diagnosis and intelligent medical treatment, considerably enhancing the effectiveness and accuracy of diagnosis [1]. It can extract meaningful semantic information from original medical image data, identify pixels of diseased organs, and obtain information features of these diseased parts [2; 3; 4]. Early medical image segmentation methods, such as template matching technology, edge detection, and traditional machine learning technology, have achieved certain results; however, due to the difficulty of feature representation, medical image segmentation is still a challenging problem. Recently, good progress has been made in medical image segmentation based on deep learning, which has aroused a lot of attention. Among them, the method based on Convolutional Neural Network (CNN) is widely used in medical image segmentation and has achieved good results. Based on the AlexNet [5] network structure, the Fully Convolutional Network (FCN) [6] converts the full connection layer into the convolutional layer, and increases the dimensionof the feature map by means of up-sampling, so as to realize the end-to-end semantic segmentation at the pixel level. However, this method has limited capability in capturing fine-grained features. This creates a significant challenge for tasks that require precise segmentation (e.g., medical image segmentation). [PERSON] et al. [7] proposed a landmark U-net network architecture on the basis of FCN. The encoding stage and decoding stage that correspond to each other in the internal components form the overall architecture of the network. In the encoding process, image features are extracted from the down-sampled images. In the process of decoding, images are up-sampled to gradually recover the size of the image [8]. The pooling operation and continuous convolution kernel in the encoding stage result in the loss of some feature information of the image. However, in the decoding stage, the up-sampled feature map merges with the front-end information from the skip connections, thereby enriching the image's detailed features. This U-shaped structure makes U-Net achieve a major breakthrough in various image segmentation work and produces many related research algorithms. For example, Unet3+ [9] and Attention U-net [10] enable the decoder to integrate more comprehensive feature information by adding the feature information of different scales or introducing attention mechanisms. The V-Net [11] network integrates 3D convolution and U-Net architecture, and proposes a volumetric full-convolution 3D neural network segmentation method, which has achieved good results on prostate MRI images. Although the CNN-based method has good local feature representation ability, due to the inherent restrictions of convolution transportation, it is challenging to learn global information and contextual semantic information interaction, so U-Net has certain limitations in remote relationship modeling and complex prediction tasks [12; 13]. Some work has adopted the deep convolutional layer to expand the receptive field, introduce an attention mechanism into network structure, or adopt a pyramid network [14; 15; 16; 17], etc., which may lead to further increases in model complexity and processing cost, and still need to further improve the ability of global information interactive capture. Motivated by the prosperous implementation of Transformer in work related to NLP, the ViT [18] algorithm makes only minor changes to the whole image classification process in an effort to directly apply the typical Transformer structure to pictures. The entire image will be broken up into smaller image blocks as part of the ViT algorithm, and the linear embedding sequence of these smaller image blocks will then be supplied to the net as the input of Transformer. When compared to other convolution-based algorithms, it reaches state-of-the-art (SOTA) performance.