Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKhoshkhabar, M-
dc.contributor.authorMeshgini, S-
dc.contributor.authorAfrouzian, R-
dc.contributor.authorDanishvar, S-
dc.identifierORCID iD: Sebelan Danishvar
dc.identifier.citationKhoshkhabar, M. et al. (2023) 'Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network', Sensors, 23 (17), pp. 7561 - 7561. doi: 10.3390/s23177561.en_US
dc.descriptionData Availability Statement: No new data were created.en_US
dc.description.abstractCopyright © 2023 by the authors. 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.en_US
dc.description.sponsorshipThis research received no external funding.en_US
dc.format.extent7561 - 7561-
dc.subjectChebyshev graph convolutionen_US
dc.subjectCT imagesen_US
dc.subjectdeep learningen_US
dc.subjectliver segmentationen_US
dc.titleAutomatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Networken_US
pubs.publication-statusPublished online-
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf8.95 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.