Research output per year
Research output per year
Reza Piri*, Lars Edenbrandt, Måns Larsson, Olof Enqvist, Amalie Horstmann Nøddeskou-Fink, Oke Gerke, Poul Flemming Høilund-Carlsen
Research output: Contribution to journal › Journal article › Research › peer-review
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 18F-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.
Original language | English |
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Journal | Journal of Nuclear Cardiology |
Volume | 29 |
Issue number | 4 |
Pages (from-to) | 2001-2010 |
ISSN | 1071-3581 |
DOIs | |
Publication status | Published - Aug 2022 |
Research output: Thesis › Ph.D. thesis