# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 2 Literature Methods  
In recent years, various machine-learning techniques have been utilized to segment the liver and tumors in medical images [4]. Additionally, [PERSON] and coauthors introduced an adaptive algorithm based on the fast marching method (FMM) for fully automatic liver segmentation. Their method dynamically adjusts parameters by considering the intensity statistics within the potential liver region, as detailed in their research [5]. Researchers have developed methods for identifying liver tumors in systems by using deep learning models, such as CNNs. They classified sets of functions into predefined or undefined classes using both supervised and unsupervised methods. Training and test data are needed to train and assess a classifier's output [6]. Regarding medical image analysis, professionals who label a set of objects--differentiating between normal and diseased cells--usually provide training data. In this study, 79 HE-stained tissue samples were used; 48 of these samples were HCC tissue, and the remaining 31 were normal tissue. The researchers aimed to distinguish between malignant and normal hepatocellular carcinoma (HCC) liver tissues and proposed a method to identify it. Deep learning approaches have been found to have good learning capacities for detecting liver tumors [7]. To detect liver tumors, several deep learning models, \"including stacked autoencoders (SAEs), convolutional neural networks (CNNs), deep Boltzmann machines (DBMs) and deep belief networks, have been used\" [3; 4; 5; 6; 7; 8]. Various CNN architectures [9; 10] have been used; however, some studies have used the VGG16 architecture, while others have used a two-dimensional (2D) UNet [3; 11; 12; 13; 14] architecture, which is frequently employed for segmenting medical images [15]. The liver and its surrounding organs have low contrast intensities, challenging liver segmentation from CT images. Recent techniques for image segmentation in medicine have used deep neural networks [16; 17] and proposed a high-quality memory network for interactive 3D medical image segmentation. It uses an extended memory network to swiftly encode and retrieve segments from the past to segment new slices. [PERSON]. [18] eliminated tiny missing lesions from the data to speed up calculation and increase accuracy using two trained deep CNN models. [PERSON]. [19] employed a convolutional neural network using image patches to assess each pixel as either cancerous or benign liver tissue. It was deemed positive if a patch had at least 50% or more tumor tissue. [PERSON]. [20] integrated a 2D DenseNet for merging intra-slice features derived from the data with a 3D counterpart for hierarchical aggregation of volumetric context for liver and lesion segmentation. Network In a study by Ref. [7], liver tumors were segmented using a cascaded architecture that combined long or short skip couplings with soft and hard focusing approaches. A standard function for Dice loss is used to decrease the number of false positives. Ref. [21] used UNet modules to integrate spatial stream convolution to create an edge system. Furthermore, the liver segmentation methods used today may not be appropriate for segmenting interventional CT images because they are meant for segmenting diagnostic CT images. Moreover, low subtle brightness, roughness, and other anomalies are frequently visible in CT images. The currently used tumor segmentation techniques have achieved varying degrees of success in resolving these difficult issues. Ref. [22] employed UNet in combination with recurrent nerve block Ref. [23] to overcome the shortcomings of the most advanced models and schemes. UNet will employ a recurrent neural network to gather pertinent liver area data and support better liver and liver segmentation techniques. Ref. [24] constructed a graphical model of problems for retinal image segmentation and obtained an accuracy of 0.99. Ref.