{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:08:33Z","timestamp":1778346513015,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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 = \u22124 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.<\/jats:p>","DOI":"10.3390\/s23177561","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T09:12:22Z","timestamp":1693559542000},"page":"7561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Maryam","family":"Khoshkhabar","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Meshgini","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Afrouzian","sequence":"additional","affiliation":[{"name":"Miyaneh Faculty of Engineering, University of Tabriz, Miyaneh 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-0437","authenticated-orcid":false,"given":"Sebelan","family":"Danishvar","sequence":"additional","affiliation":[{"name":"College of Engineering, Design, and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"216048","DOI":"10.1016\/j.canlet.2022.216048","article-title":"Human liver cancer organoids: Biological applications, current challenges, and prospects in hepatoma therapy","volume":"555","author":"Chen","year":"2023","journal-title":"Cancer Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shahini, N., Bahrami, Z., Sheykhivand, S., Marandi, S., Danishvar, M., Danishvar, S., and Roosta, Y. 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