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dc.contributor.authorKim, Taehun-
dc.contributor.authorOn, Sungchul-
dc.contributor.authorGwon, Jun Gyo-
dc.contributor.authorKim, Namkug-
dc.date.accessioned2024-05-30T08:30:50Z-
dc.date.available2024-05-30T08:30:50Z-
dc.date.created2024-05-30-
dc.date.issued2024-04-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/149955-
dc.description.abstractAccurate measurement of abdominal aortic aneurysm is essential for selecting suitable stent-grafts to avoid complications of endovascular aneurysm repair. However, the conventional image-based measurements are inaccurate and time-consuming. We introduce the automated workflow including semantic segmentation with active learning (AL) and measurement using an application programming interface of computer-aided design. 300 patients underwent CT scans, and semantic segmentation for aorta, thrombus, calcification, and vessels was performed in 60-300 cases with AL across five stages using UNETR, SwinUNETR, and nnU-Net consisted of 2D, 3D U-Net, 2D-3D U-Net ensemble, and cascaded 3D U-Net. 7 clinical landmarks were automatically measured for 96 patients. In AL stage 5, 3D U-Net achieved the highest dice similarity coefficient (DSC) with statistically significant differences (p < 0.01) except from the 2D-3D U-Net ensemble and cascade 3D U-Net. SwinUNETR excelled in 95% Hausdorff distance (HD95) with significant differences (p < 0.01) except from UNETR and 3D U-Net. DSC of aorta and calcification were saturated at stage 1 and 4, whereas thrombus and vessels were continuously improved at stage 5. The segmentation time between the manual and AL-corrected segmentation using the best model (3D U-Net) was reduced to 9.51 +/- 1.02, 2.09 +/- 1.06, 1.07 +/- 1.10, and 1.07 +/- 0.97 min for the aorta, thrombus, calcification, and vessels, respectively (p < 0.001). All measurement and tortuosity ratio measured - 1.71 +/- 6.53 mm and - 0.15 +/- 0.25. We developed an automated workflow with semantic segmentation and measurement, demonstrating its efficiency compared to conventional methods.-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.titleComputed tomography-based automated measurement of abdominal aortic aneurysm using semantic segmentation with active learning-
dc.typeArticle-
dc.identifier.doi10.1038/s41598-024-59735-8-
dc.description.journalClass1-
dc.identifier.bibliographicCitationScientific Reports, v.14, no.1-
dc.citation.titleScientific Reports-
dc.citation.volume14-
dc.citation.number1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid001207621600068-
dc.identifier.scopusid2-s2.0-85190662435-
dc.relation.journalWebOfScienceCategoryMultidisciplinary Sciences-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.type.docTypeArticle-
dc.subject.keywordAuthorAbdominal aortic aneurysm-
dc.subject.keywordAuthorActive learning-
dc.subject.keywordAuthorApplication programming interface-
dc.subject.keywordAuthorComputer-aided design-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorEndovascular abdominal repair stent graft-
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