# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 1 Introduction  
Medical image segmentation is an important task in medical image processing and analysis. It has great application and research value in medical research [1], clinical diagnosis, pathological analysis, surgical planning, computer-assisted surgery [2], and so on. The purpose of the medical image segmentation task is to extract and segment special features, such as lesions, and to provide a reliable basis for clinical diagnosis and pathological research. The main challenges of medical image segmentation are as follows: first, it is very difficult to construct a database of medical images, because the medical images themselves are extremely unbalanced, with many normal samples and few and variable lesion samples, which leads to insufficient well-labeled training samples [3]. There are still some problems, such as limited image quality, an absence of universally adopted segmentation protocols, and significant inter-patient variations in image characteristics [4]. In addition, medical images generally have a lot of noise and artifacts in imaging. The quantification of segmentation accuracy and uncertainty is critical for estimating its performance in other applications [5]. There are many types of medical images, including computed tomography, X-ray, magnetic resonance imaging, and positron emission computed tomography images. Medical image segmentation mainly includes methods based on thresholds, regions, deformation models, and fuzzy and neural networks. In recent years, deep learning technology has played an important role in the field of computer vision, and it is rapidly being applied to other fields, especially in the fieldof medicine. FCN [6] stands out as one of the initial deep learning architectures specifically designed for end-to-end training, which enables pixel-wise predictions in semantic segmentation tasks. U-Net [7] is a network with an end-to-end encoder-decoder structure, and it overcomes the shortcoming that FCN cannot keep some pixel spatial position information and context information, which leads to the loss of local features and global features. [PERSON] et al. [8] proposed that the U-shaped end-to-end network is the best medical image segmentation architecture. The U-Net structure includes two stages: the first stage is the down-sampling process, which is mainly responsible for feature extraction, and the second stage is the up-sampling process based on the first stage, which is responsible for outputting the feature map with the same size as the original sample. Because the features obtained in the first stage comprise highly compressed feature information, and the task of image segmentation needs to classify each pixel in the image, it cannot completely rely on up-sampling features, and it needs to be supplemented with the information in the down-sampling process. The skip-connection operation is used in U-Net. On the basis of U-Net, many people have improved this network. For example, [PERSON] et al. [9] proposed U-Net++. This network changes the original U-Net layers, redesigns the skip-connection in the original network, and adds depth supervision to different segmentation tasks to achieve a good segmentation result. Because of the excellent performance of U-Net in medical image segmentation tasks, this framework is widely used for lesion segmentation in organs, such as the eyes, heart, liver, brain, skin, prostate, and breast. [PERSON] et al. [10] proposed ANU-Net for the organ cancer segmentation task. The network has achieved good segmentation results on the LiTS (liver tumor segmentation) dataset and the CHAOS (combined healthy abdominal organ segmentation) dataset with dense skip-connection and an attention mechanism. [PERSON] et al. [11] proposed DIU-Net, which integrates the inception module and the dense skip-connection module into U-Net to increase its width and depth. This network is used to segment the brain tumors of MRI images and computed tomography images of lungs and retinal blood vessels, and it achieves good results. [PERSON] et al.