## 2 Related Work
### The Methods Based on Deep Learning  
In recent years, deep learning methods have achieved great success in many tasks of the computer vision field, such as classification, segmentation and detection [32; 33]. For example, the end-to-end convolutional neural network can continuously explore and learn new image feature representations and can classify each pixel in the image to achieve image segmentation. In the early days, the methods of most researchers obtain tissues and organs in medical images by performing patchwise image classification [34]. These segmentation methods, which only consider the local context information, easily fail in challenging modalities, such as MRI, since there are too many misclassified voxels in the image. Patchwise approaches [35] can obtain more accurate segmentation results by constantly putting forward the proposed region input into CNN. However, many calculations are redundant when dealing with patched extracted intensively by CNNs; therefore, their total running time is too long [36]. Based on the emergence of end-to-end convolutional neural networks, more image feature representations can be continuously explored. For instance, each pixel in the image can be classified to achieve the goal of automatic image segmentation [7]. Considering the excellent effect of deep convolutional neural networks, many researchers have designed various networks to segment the liver and tumor by using the strong learning ability of convolutional neural networks. For instance, Many researchers have combined conditional random fields (CRFs) with deep learning techniques and applied them in medical image segmentation. 3D CRFs is a conditional probability distribution model for a given set of input sequences and another set of output sequences. As each voxel \(i\) in 3D data has a corresponding category \(y_{i}\), each pixel is taken as a node and the relationship between pixels is taken as an edge to form a conditional random field. It can be used as a post-processing method to enhance deep learning. The essence of CRFs is to predict its true category \(y_{i}\) by observing the probability \(x_{i}\) of voxel \(i\). [PERSON] et al. [9; 37] designed cascaded FCNs for the segmentation of the liver and tumor and used 3D (CRFs) for postprocessing. [PERSON] et al. [38] proposed a novel superpixel-basedconditional random fields (SuperCRF) that could improve the classification of the cells by incorporating global and local context for enhanced deep learning. CRFs can also integrate into deep learning as a network module. In [39], [PERSON] et al. formulated CRFs with Gaussian pairwise potentials and mean-field approximate inference as RNNs. Then they plugged the RNNs into CNN and applied this method to the problem of semantic image segmentation, obtaining top results on the VOC dataset. [PERSON] et al. [40] also incorporated CRFs into an end-to-end network for medical image segmentation. In addition, many deep learning methods to enhance feature expression have also been proposed for medical image segmentation. For example, [PERSON] et al. [41] proposed a receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. [PERSON] et al. [42] proposed an encoding and decoding neural network to segment tumors, which used the attention network to mixture the high and low-level image features and improve the segmentation accuracy. The structure and dimension of networks also play an important role in medical image segmentation. [PERSON] et al. [43] proposed an end-to-end 3D fully convolutional network for lesion segmentation in prostate CT data, which can fully explore the 3D context information and use the similarity coefficient to optimize the network during training. [PERSON] et al. [18] proposed a 2D and 3D hybrid dense network to segment the liver and tumor. [PERSON] et al.