{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lee SB"],"funding":["National Research Foundation of Korea"],"pagination":["294-304"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10067697"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(4)"],"pubmed_abstract":["<h4>Objective</h4>We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods.<h4>Materials and methods</h4>We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A"],"journal":["Korean journal of radiology"],"pubmed_title":["Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation."],"pmcid":["PMC10067697"],"funding_grant_id":["NRF-2020R1F1A1077320"],"pubmed_authors":["Lee S","Cheon JE","Yoon SH","Hong Y","Choi YH","Cho YJ","Jeong D","Lee J","Lee SB"],"additional_accession":[]},"is_claimable":false,"name":"Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.","description":"<h4>Objective</h4>We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods.<h4>Materials and methods</h4>We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-04T10:42:05.085Z","creation":"2025-04-04T10:42:05.085Z"},"accession":"S-EPMC10067697","cross_references":{"pubmed":["36907592"],"doi":["10.3348/kjr.2022.0588"]}}