Automatic segmentation and determining radiodensity of the liver in a large-scale CT database

• URL: http://arxiv.org/abs/1912.13290v1
• Date: Tue, 31 Dec 2019 12:41:05 GMT
• Title: Automatic segmentation and determining radiodensity of the liver in a large-scale CT database
• Authors: N. S. Kulberg (1 and 3), A. B. Elizarov (1), V. P. Novik (1), V. A. Gombolevsky (1), A. P. Gonchar (1), A. L. Alliua (2), V. Yu. Bosin (1), A. V. Vladzymyrsky (1), S. P. Morozov (1) ((1) State Budget-Funded Health Care Institution of the City of Moscow Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, (2) Federal State Budgetary Scientific Institution Russian Scientific Center of Surgery named after Academician B.V. Petrovsky, (3) Federal Research Center Computer Science and Control of Russian Academy of Sciences)
• Abstract summary: The technique can be used to process CT images obtained in various patient positions in a wide range of exposition parameters. It is capable of dealing with low dose CT scans in real large-scale medical database with over 1 million of studies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study proposes an automatic technique for liver segmentation in computed tomography (CT) images. Localization of the liver volume is based on the correlation with an optimized set of liver templates developed by the authors that allows clear geometric interpretation. Radiodensity values are calculated based on the boundaries of the segmented liver, which allows identifying liver abnormalities. The performance of the technique was evaluated on 700 CT images from dataset of the Unified Radiological Information System (URIS) of Moscow. Despite the decrease in accuracy, the technique is applicable to CT volumes with a partially visible region of the liver. The technique can be used to process CT images obtained in various patient positions in a wide range of exposition parameters. It is capable in dealing with low dose CT scans in real large-scale medical database with over 1 million of studies.

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