在计算机断层扫描（CT）图像中分割肝脏和肝肿瘤是实现计算机辅助决策系统和精确医学诊断的可量化生物标志的重要步骤。放射科医生和专业医师使用CT图像来诊断和分类肝脏器官和肿瘤。由于这些器官在形态、纹理和亮度数值上具有相似特征，其他内脏器官如心脏、脾脏、胃和肾脏会干扰对肝脏和肿瘤区分的视觉识别。此外，肝肿瘤的视觉识别耗时、复杂且容易出错，错误的诊断和分割可能会危及患者的生命。近年来，基于机器学习算法的许多自动化和半自动化方法已被提出用于肝脏器官识别和肿瘤分割。然而，由于识别精度和速度差、缺乏可靠性等原因仍存在困难。本文提出了一种基于深度学习的新技术，用于在计算机断层扫描图上分割肝脏肿瘤和识别肝脏器官。根据LiTS17数据库，建议的技术包括四个切比雪夫图卷积层和一个全连接层，能够准确地分割肝脏和肝肿瘤。因此，根据LiTS17数据集，基于所提出的方法得到的准确度、Dice系数、均值交并比、敏感度、精确度和召回率分别约为99.1%、91.1%、90.8%、99.4%、99.4%和91.2%。此外，提出的方法在噪声环境下进行了评估，该网络能够适应广泛的环境信噪比（SNR）。因此，在SNR = -4 dB时，所提出的方法对肝脏器官分割的准确度仍在90%左右。与先前的研究相比，所提出的模型获得了令人满意和有利的结果。根据这些积极的结果，预计该提议的模型将在不久的将来被用于辅助放射科医生和专科医生的工作。

Segmenting the liver and liver tumors in computed tomography (CT) images is an important step toward quantifiable biomarkers for a computer-aided decision-making system and precise medical diagnosis. Radiologists and specialized physicians use CT images to diagnose and classify liver organs and tumors. Because these organs have similar characteristics in form, texture, and light intensity values, other internal organs such as the heart, spleen, stomach, and kidneys confuse visual recognition of the liver and tumor division. Furthermore, visual identification of liver tumors is time-consuming, complicated, and error-prone, and incorrect diagnosis and segmentation can hurt the patient's life. Many automatic and semi-automatic methods based on machine learning algorithms have recently been suggested for liver organ recognition and tumor segmentation. However, there are still difficulties due to poor recognition precision and speed and a lack of dependability. This paper presents a novel deep learning-based technique for segmenting liver tumors and identifying liver organs in computed tomography maps. Based on the LiTS17 database, the suggested technique comprises four Chebyshev graph convolution layers and a fully connected layer that can accurately segment the liver and liver tumors. Thus, the accuracy, Dice coefficient, mean IoU, sensitivity, precision, and recall obtained based on the proposed method according to the LiTS17 dataset are around 99.1%, 91.1%, 90.8%, 99.4%, 99.4%, and 91.2%, respectively. In addition, the effectiveness of the proposed method was evaluated in a noisy environment, and the proposed network could withstand a wide range of environmental signal-to-noise ratios (SNRs). Thus, at SNR = -4 dB, the accuracy of the proposed method for liver organ segmentation remained around 90%. The proposed model has obtained satisfactory and favorable results compared to previous research. According to the positive results, the proposed model is expected to be used to assist radiologists and specialist doctors in the near future.