## 1 Introduction  
Cancer is one of the most fatal and widespread diseases worldwide. Patients can greatly extend their survival time and increase their survival rate with the help of early detection. CT scan is the most commonly used clinical auxiliary method for cancer diagnosis. Physicians make clinical diagnosis on patients through the results of their CT images.However, due to the uneven regional distribution of medical resources, physicians with the abovementioned diagnostic capabilities also show obvious uneven distribution. At the same time, the distribution of cancer patients is relatively random. Therefore, patients in rural areas can only go to central cities for disease diagnosis and treatment, which greatly increases the work intensity of these physicians. However, the complexity of the medical image itself and the high requirements for accuracy of the segmentation result require physicians to perform detailed analysis for a long time. This contradiction has become one of the most urgent problems in cancer diagnosis worldwide. To reduce the workload of physicians and to improve work efficiency, there is a necessity for a high-precision automatic segmentation model to assist physicians in completing medical CT image segmentation tasks [1]. With the continuous expansion of deep learning applications, convolutional neural network (CNN)-based image semantic segmentation models [2; 3; 4; 5] have become a better choice to solve the above problems. However, it is not feasible to directly transfer the segmentation model that has been successful in natural image segmentation to the medical image segmentation task because medical images have many characteristics:  
* High-quality medical image datasets are rare. The special imaging method of medical images produces less data but more false positive phenomena. At the same time, annotating requires countless time, and this process is easily affected by subjective factors such as doctor experience. * Medical images are data with higher dimensions. Compared with the two-dimensional data of natural images, common medical images such as CT and MRI are three-dimensional or higher-dimensional volume data, which is an ordered collection of several two-dimensional images. This makes it impossible to directly read high-dimensional medical image data into the memory, and it is difficult to directly use the natural image segmentation model for training. * The target is smaller in the medical image segmentation, and the boundary of objects is blurred. Compared with objects in natural images, human organs or tumors occupy a very small proportion in medical images, the appearance of the same target under different viewing angles is very different, and the difference between the target and the background is small. The above factors all make the labeling and segmentation of the target extremely difficult. * There is a lack of general pretrained models in this field. The famous ImageNet pretrained model in natural images cannot be transferred to medical image segmentation tasks, and there is insufficient data in medical images to support a pretrained model suitable for most tasks. It was the fully convolutional neural network proposed by [PERSON] [6] of UC Berkeley and the U-Net proposed by [PERSON] [7] that brought substantial changes to medical image semantic segmentation. Many researches [8; 9; 10; 11; 12; 13; 14; 15; 16; 17] were studied based on this work. However, these models usually pay more attention to the overall characteristics of the target, thus ignoring the importance of boundary in medical image segmentation. Such designs are likely to result in a high evaluation indicator, but this high indicator does not represent accurate segmentation of medical images because it does not perform well on boundary segmentation. The rigor of medicine requires higher accuracy of boundaries, especially in human organ image segmentation. Even a small boundary error can lead to misdiagnosis, leading to further work up or unnecessary procedures.