# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 1 Introduction  
Malignant liver neoplasm is one of the most common types of human cancer and is responsible for the high death rate due to its late diagnosis. Liver disease had the sixth-highest occurrence of all cancers, and the fourth-highest mortality rate in the world, with around 841,000 new cases at a rate of 93 cases per million people and about 782,000 deaths at a rate of 85 cases per million people in 2020 [1]. As tumors can be detected in medical images, the focus of recent research has been shifted towards the segmentation of liver and tumor. It is evident from the fact that the number of studies based on segmentation of liver and tumor has doubled recently [2]. Although the windowed Hounsfield unit values may provide a range of values for the marking of different regions in medical images, identifying voxels that portray liver and lesion regions is challenging due to the low contrast between the liver and the neighbouring organs [3]. Owing to the different sizes, form, and locations of lesions, liver tumor segmentation becomes more complex and challenging in different patients [4]. Furthermore, the margins of some lesions are imprecise, such as hazy, makingit hard to detect using edge-based segmentation algorithms. It warrants the need for an algorithm using artificial intelligence that may help medical physicians segment organs and malignancies. In addition, computed tomography (CT) has become one of the most commonly used imaging technologies for identifying and detecting liver tumors because of its excellent spatial precision and speedy laser power. In typical clinical operations, segmentation can be done manually by operators with sufficient competence, but it is time-consuming, and results from different operators are sometimes inconsistent. As a result, developing an automatic segmentation method is extremely difficult, because of the variable forms of tumours, the vast range of tumor intensities, and the uncertainty of tumor and adjacent normal hepatic tissues borders. Over the last few years, semantic segmentation of CT images has become a hot topic of study. Deep learning has significantly enhanced artificial intelligence performance in recent years. In medical imaging, deep learning methods, especially fully convolutional neural network, have surpassed their competitors significantly. In this article, we have implemented ResU-Net architecture for liver and liver tumor segmentation in CT Scan images. The training and testing of the ResU-Net are conducted with selected image pre-processing techniques, Housnfiled windowing unit, histogram equalization and data augmentation methods.  
Our contribution to this work includes the implementation of U-Net architecture along with residual blocks. The ResU-Net architecture was trained using selected image pre-processing techniques, Hounsfield Windowing, methods for adjusting the brightness level of CT scan, data augmentation for increasing the number of images and their variations, and a method to tackle the issue of class imbalance. The weighted cross-entropy loss function was also used during network training to address the class imbalance issue. The proposed methodology has obtained better results for liver and liver tumor as assessed by DSC, accuracy, precision, specificity, VOE and RVD values.  
The paper is outlined as follows. In Section 2, we have analysed state-of-the-artwork related to the liver and liver tumor segmentation. This section also includes a summary of our contribution. Next, Section 3 presents materials and methods, and Section 4 presents result and discussion. Lastly, Section 5 concludes the paper and suggests further work in this area.