# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 1 Introduction  
Image segmentation plays a crucial role in computer vision. In the medical field, this technology can be applied to different areas. Liver cancer is the second most commonly diagnosed disease in the world. According to the World Health Organization (WHO), an estimated 8.8 million people died from this disease in 2015, and approximately 788,000 people died from cancer [1]. In the United States, the American Cancer Society predicts that an estimated 20,710 new cases (29,200 in males and 11,510 in females) will be diagnosed annually. In 2017, 28,920 individuals (9310 females and 19,610 males) died from intrahepatic epithelial duct cancer and primary cancer [2]. Radiologists and oncologists use computed tomography (CT) or magnetic resonance imaging (MRI) to examine the structure and texture of the liver. These abnormalities are vital biomarkers for diagnosing and monitoring primary and secondary hepatic tumors. Historically, manual or semi-manual methods have been used to analyze liver CT volume scans, but these methods are expensive, time-consuming, and prone to errors. Researchers have developed various computational techniques to improve the accuracy and efficiency of diagnosing liver cancer, but these techniques stillface challenges and difficulties. Obstacles include the inability to precisely segment and detect liver lesions due to factors such as irregular tumor growth, tissue abnormalities, minimal contrast between healthy and cancerous tissue distinction between the liver and the surrounding organs, and variations in the quantity and size of tumors. Oncologists and radiologists used magnetic resonance imaging (MRI) and computed tomography (CT) to determine the anatomy and physiology of the liver. These anomalies were found to be essential biomarkers for the early diagnosis of both primary and secondary liver cancer [3]. The convolution neural network model is known for its efficiency in analyzing different appearances of images. Our goal in this paper is to improve previous research by evaluating the performance of a framework model trained by deep learning techniques on a diverse set of CT images of tumors taken from the MSD Task03 Liver dataset. To achieve this, we propose a new method that overcomes the challenges in the field. The primary objective is to develop a stronger and more capable model based on deep learning to identify regions of interest (ROIs) in images accurately. This approach can be used for both feature classification and automated feature extraction. Earlier studies had some drawbacks as they used images from enhanced magnetic reasoning and CT, and only a few types of liver tumors were treated with the CNN method. Our current research goal is to develop a fully automated system that can quickly and accurately detect and segment liver tumors with minimal effort and time. Automatic procedures can adapt over time, regardless of how multiple inputs and scenarios are integrated. Medical imaging extensively utilizes image segmentation techniques to identify and isolate regions of interest, such as cancer cells, tumor tissues, or lesions. The results obtained from segmentation are critical and require high accuracy, as they directly impact the diagnosis and treatment prescribed to patients. Researchers are actively working to improve the effectiveness of image segmentation for medical imaging applications. Therefore, the segmentation of images is a challenging task, and choosing the appropriate technique for segmenting a digital image for a specific application remains an active area of research. There are two types of image segmentation: semantic and instance segmentation. The main difference between the two types is that semantic segmentation can only produce pixel probabilities if a pixel belongs to one of two classes. The objective of instance segmentation is object indexing, regardless of whether the objects belong to the same class. In our case, we use semantic segmentation because we need to discover whether the slices can contain a liver or not.