# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 2 Related Work  
Over the past decade, researchers have used different machine learning strategies to separate liver and tumors. [PERSON] et al. [5] proposed an autonomous 3D liver segmentation approach that uses a total variation with the L1 norm (TV-L1) to detect an initial liver border, followed by a level set method that uses both local and global energy functions. A texture analysis method based on the grey level co-occurrence matrix (GLCM) is used for refined segmentation. [PERSON] et al. [6] proposed a multi-atlas segmentation (MAS) based performance level estimation (SIMPLE) method for automatic segmentation of abdominal organs, including the liver. [PERSON] et al. [7] developed an adaptive FMM-based fully automatic liver segmentation algorithm. Self-adaptive parameter adjustment is used in the proposed adaptive FMM. In FMM, the arrival time is adjusted based on the intensity statistics of the potential liver region, which can be used to estimate the size of the liver region on the corresponding computed tomography (CT) slices. [PERSON] et al. [8] developed a semi-automated method for fragmenting the hepatic low-intensity tumor using CT images that utilized abdominal arterial channels in the entrance stage. [PERSON] et al. [9] proposed a finite difference energy technique that incorporates brightness, region attractiveness, or surface smoothing. Automatic and robust liver segmentation from CT volumes is challenging due to the low-intensity contrast between the liver and neighbouring organs. Deep neural networks are used extensively in present healthcare image segmentation frameworks [10; 11]. [PERSON] et al. [12] introduced quality-aware memory network for interactive segmentation of 3D medical images. The memory-augmented network can quickly encode and retrieve segments from the past for segmentation of new slices. [PERSON] et al. [13] used two trained deep CNN models to reduce the computational time of a large number of slices. After obtaining the liver segmentation with the first model, the second model was used to avoid the impact of image resampling by removing small missing lesions. [PERSON] et al. [14] employed a convolutional neural network (CNN) that used image patches. Each pixel is considered as a patch of the image, with the pixel of interest at its center. It divides the patches into normal or tumor liver tissues. The patch is considered positive if it contains at least 50 percent or more tumor tissue. [PERSON] et al. [15] merged a 2D DenseNet network that combined the extracted intra-slice features and the 3D counterpart for hierarchically aggregating volumetric contexts for liver and lesion segmentation. The liver tumour was segmented by [PERSON] et al. [16] using a cascading architecture in which soft and harsh focus techniques and long or short skip linkages were integrated. False positives were reduced by using a joint dice loss function. [PERSON] et al. [17] developed an edge system by incorporating spatial stream convolution into the U-Net network's modules. Furthermore, current liver segment techniques are designed for segmenting diagnostic CT pictures and may not even be suitable for segmenting interventional CT images. In addition, CT scans frequently show low gentle brightness, roughness, and other abnormalities. Current tumor segmentation approaches have had mixed results in resolving these challenging problems. As a result, U-Net was used by [PERSON] et al. [18] in conjunction with a recurrent neural block [19] to resolve the limitation of the state-of-the-art models and schemes. The U-Net will use the recurrent neural network to gain relevant data about the hepatic area, aiding in improved liver and hepatic segmentation methods. [PERSON] et al. [20] implemented probabilistic graphical model for segmentation for fundus images and obtained accuracy of 0.99. [PERSON] et al. [21] implemented a Fully Convolutional Network (FCN) to better segment the brain MRI having a varying size and shape ischemic lesion and obtained the DSC score of 0.75. [PERSON] et al.