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Aortic wall segment...
Aortic wall segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation
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- Piri, Reza (author)
- Odense University Hospital,Syddansk Universitet,University of Southern Denmark
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- Edenbrandt, Lars, 1957 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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Larsson, Mans (author)
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- Enqvist, Olof, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Noddeskou-Fink, Amalie Horstmann (author)
- Odense University Hospital
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- Gerke, Oke (author)
- Odense University Hospital,Syddansk Universitet,University of Southern Denmark
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- Hoilund-Carlsen, Poul Flemming (author)
- Syddansk Universitet,University of Southern Denmark,Odense University Hospital
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(creator_code:org_t)
- 2021-05-12
- 2022
- English.
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In: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1532-6551 .- 1071-3581. ; 29:4, s. 2001-2010
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Abstract
Subject headings
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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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk laboratorie- och mätteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Laboratory and Measurements Technologies (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Keyword
- artificial intelligence
- bias
- sodium fluoride
- PET
- CT
- aorta
- aorta
- artificial intelligence
- bias
- PET/CT
- sodium fluoride
Publication and Content Type
- art (subject category)
- ref (subject category)
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