Search: id:"swepub:oai:research.chalmers.se:854c47be-697e-4f59-aa45-b52623333234" >
Aortic wall segment...
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Piri, RezaOdense University Hospital,Syddansk Universitet,University of Southern Denmark
(author)
Aortic wall segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation
- Article/chapterEnglish2022
Publisher, publication year, extent ...
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2021-05-12
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Springer Science and Business Media LLC,2022
Numbers
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LIBRIS-ID:oai:research.chalmers.se:854c47be-697e-4f59-aa45-b52623333234
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https://research.chalmers.se/publication/524136URI
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https://doi.org/10.1007/s12350-021-02649-zDOI
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https://gup.ub.gu.se/publication/308937URI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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Background We aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. Methods For segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on F-18-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by manual segmentation. The following derived parameters were compared using Bland-Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the manual method was examined in 25 randomly selected scans. Results CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat manual segmentation was 6%. Conclusions The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the manually obtained values, except for SUVmean values, which were comparable. AI-based segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome manual segmentation.
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Edenbrandt, Lars,1957Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine(Swepub:gu)xedenl
(author)
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Larsson, Mans
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Enqvist, Olof,1981Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)enolof
(author)
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Noddeskou-Fink, Amalie HorstmannOdense University Hospital
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Gerke, OkeOdense University Hospital,Syddansk Universitet,University of Southern Denmark
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Hoilund-Carlsen, Poul FlemmingSyddansk Universitet,University of Southern Denmark,Odense University Hospital
(author)
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Odense University HospitalSyddansk Universitet
(creator_code:org_t)
Related titles
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In:Journal of Nuclear Cardiology: Springer Science and Business Media LLC29:4, s. 2001-20101532-65511071-3581
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