# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 1 Introduction  
Medical image segmentation is a basic and challenging task in CAD, whose goal is to segment target organs, tissues or diseased regions accurately. Because of the limitations of imaging technology and imaging devices, medical images always have complex textures, unlike natural scenes, and have boundaries blurred with noise. In addition, medical image annotations rely heavily on the knowledge and experience of experts, so the annotation data size available for training is small, including annotation errors. Therefore, the CAD system which is based on the traditional image segmentation algorithm, can hardly meet the requirements of medical applications. Nowadays, compared with traditional image segmentation methods which are based on thresholding, edge, region, or graph theory, methods based on deep learning have improved segmentation accuracy and have significant advantages in medical and complex image segmentation. Image segmentation methods based on deep learning can process various image datatypes. A deep neural network (DNN) was used to segment biological neuron membranes for electron microscopy (EM) images [1]. An automatic discrimination model based on a full convolutional network (FCN) and transfer learning was used to segment lung regions for X-ray images, and the accuracy was more than 80% [2]. An adaptive full-density neural network was proposed for CT image segmentation by adding horizontal connections to the U-Net model, which can extract more edge information from the image [3]. Furthermore, even a network-combined CNN and LSM can be applied to segment polarimetric synthetic aperture radar images [4]. Specifically, a variety of segmentation tasks in medical images can be processed effectively by use of deep learning networks. An end-to-end solution for multi-class semantic segmentation using FCN and transfer learning was proposed to achieve the automatic segmentation of three targets: corneal, melanoma, and benign lesion [5]. A 3D medical image segmentation method was proposed, which performed outstandingly on prostate and heart segmentation [6]. A new U-Net architecture was built by integrating inception-resnet with CNN, and replacing standard convolutional blocks with inception-resnet, which can achieve an average Dice score of 0.9857 or higher on lung and brain tumor segmentation [7]. Super-pixel and CNN networks were combined to segment cervical cancer cell images, which can reach 94.50% accuracy in core region detection [8]. The RDCTrans U-Net was used for liver tumor segmentation, achieving SOTA results [9]. A U-Net network was applied for lung nodule segmentation, which can reach 92.11% overlap with annotation [10]. Then, the results of the lung nodule segmentation kept improving through the use of Mask R-CNN, NRU, and 3D U-Net successively [11; 12; 13]. In this current work, we will mainly focus on the CT image, which comprises a series of slice images of the human body; and provides valuable information about the internal structure and composition of organs, tissues, and bones; making it an important diagnostic tool for diagnosis, treatment, and monitoring of a wide range of medical conditions. CT images can help doctors to identify tumors and other abnormalities in the body that may not be visible with other imaging techniques. Figure 1 shows an example of the three-view drawing of the liver CT. All the slice images can form a 3D image which can be used for a variety of disease screening. These segmentation methods, through using 2D cross-section, ignore the correlation between slices. Furthermore, to minimize the loss of spatial information, more and more 3D networks were researched in order to segment images directly. A 3D Faster R-CNN model was used to generate candidate nodules by designing a deep 3D two-way network [14]. An end-to-end multitasking neural network, based on 3D V-Net, was applied to detect and segment lung nodules simultaneously [15].