# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 1 Introduction
With the deepening of the research, the combination of CNN and Transformer has achieved good results. [PERSON] et al. [19] offered the Trans-Unet method in 2021, which generates a feature graph using CNN as a tool for extracting features and feeds it into Transformer to extract global data, and uses a cascading upper sampler to ensure accurate prediction. This algorithm has achieved excellent results in multiple medical image datasets. In 2021, [PERSON] et al. proposed the Swin-Unet [20] algorithm based on Transformer to be applied to the medical image segmentation task, which significantly decreases the computational burden of the algorithm and achieves high segmentation accuracy. However, because Swin-Unet can only learn single-scale context features in the training process, the amount of computation is too large and the induction bias module for processing local information is lacking [21]. Some subsequent methods, such as nnFormer, MISSFormer, DSTransUnet, TransDeepLab, and DAE-Former [22; 23; 24; 25; 26], adopt a pure Transformer architecture. Specifically, transformer blocks are employed on both the encoder and decoder sides to capture more global characteristics and fuse multi-scale information through skip connections. The Transformer architecture model, distinguished from the local dependency bias of convolutional neural networks due to its excellent global modeling capabilities and ability to capture long-range dependencies, has garnered significant attention from researchers. However, its high computational cost has also deterred some. As a result, in recent years, research in medical image segmentation has shifted towards hybrid Transformer architectures with skip connections. [PERSON] et al. argued that segmentation methods based on patch division might disrupt local coherent features and introduce noise. Building on this premise, they proposed a progressive sampling module [27]. Unlike traditional segmentation models, this approach achieved promising results in small object segmentation tasks, although it did not perform as well in large object segmentation tasks. [PERSON] et al. innovatively combined visual transformers with graph convolution, proposing a graph-based cascaded attention architecture [28] to address the limitations of visual transformers in handling local spatial information correlations. [PERSON] et al. introduced a 3D Transformer architecture called nnFormer [29] based on multiple attention mechanisms, which balanced long-range and spatial dependencies in the self-attention mechanism. Besides, [PERSON] et al. redefined the skip connections between the encoder and decoder, enhancing global dependencies further through a bridge-like connection method [30]. Inspired by these works, we suggest an RL-Unet algorithm based on Swin-Unet to solve the aforementioned issues. It redesigns the up-sampling module and adds a local inductive bias module to the Swin-Transformer block, resulting in a densely connected double up-sampling module that can fully learn multi-scale information and increase the boundary segmentation accuracy of the algorithm. The suggested method has perfect segmentation accuracy and a strong generalization capability, according to extensive testing on the segmentation datasets for the synapse multiple abdominal organs and BraTS2021 datasets. Our contributions, specifically, can be summed up as follows:  
* We add a local induction bias module to the Swin-Transformer block to assist the RLSwin-Transformer module learn local features and remote dependencies, and replace the MLP module with the Res-MLP module to prevent feature loss during transmission, thus increasing segmentation accuracy. * We design a new double up-sampling module, which includes bilinear up-sampling and expansion convolution with different expansion rates for feature extraction and image restoration. * A dense connection structure is designed at the decoding end, and an attention mechanism is introduced to restore resolution and generate segmentation map.