## 1 Introduction  
The liver is a crucial organ in the metabolic process of the human organism, and liver tumors, as a highly prevalent disease, seriously threaten human life and health. The accurate segmentation of tumor regions from computed tomography (CT) images is an important step in the subsequent diagnostic and therapeutic phases. This process can provide doctors with more precise information about the location of lesions, enhancing diagnostic efficiency and accuracy and offering higher clinical value.  
However, it is challenging to effectively and precisely distinguish tumor areas from the background due to the diversity of tumor shapes and locations. In recent years, deep learning methods have progressively taken center stage in the segmentation of liver tumors [1]. Among them, the U-Net [2] model has proven to have a high level of segmentation capabilities. To deal with difficult segmentation tasks, scientists have created numerous U-Net variant networks.  
[PERSON] et al. [3] proposed DCMC-Unet based on two-channel multi-scale convolution for liver tumor segmentation. Meanwhile, they employed a thresholding method to eliminate extraneous tissues for noise elimination. The network can effectively extract features at multiples scales and is applicable to tumors of varying sizes and shapes. However,due to the oversimplified jump connections, the model's ability to facilitate interaction between shallow and deep information is limited. Additionally, the presence of semantic gaps reduces the model's capacity for extracting and combining features. To address this problem, [PERSON] et al. [4] designed the deep dense network ResU-Net. This replaced the convolutional layers with residual blocks, aiming to make full use of the advantages of the U-Net network and deep residual learning for liver tumor segmentation. [PERSON] et al. [5] introduced deep jump connections into U-Net to fully extract features from the encoder for enhanced feature learning. While the bottleneck layer in the aforementioned two methods is relatively simplistic, encoder features cannot be fully utilized here. This limitation can result in the loss of useful information and the degradation of network performance.  
Therefore, to solve the issues mentioned above, we introduced multi-scale feature extraction with dense connections and an attention mechanism U-Net (MDAU-Net) for liver and liver tumor segmentation. The main contributions of this work are as follows:  
1. We redesigned the jump connection and introduced a double-flow linear pooling enhancement unit (DLE) to improve the interaction ability between deep and shallow features, which helped to narrow the semantic gap.
2. To better realize the extraction and reuse of useful features, we proposed a cascaded adaptive feature extraction unit (CAE) as a substitute for the bottleneck layer. It was based on an multi-head attention mechanism and a series of dense connections.
3. We designed a cross-level information interaction mechanism (CII). It used bidirectional residual connections and was placed in the skip connection to overcome the problem of forgetting a priori knowledge in the learning process.
4. We proposed a residual encoder to bolster the preservation of original features and supply additional initial information for the segmentation task.