Aortic wall segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation
Journal article, 2021

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.

artificial intelligence

aorta

bias

sodium fluoride

PET

CT

Author

Reza Piri

Odense University Hospital

University of Southern Denmark

Lars Edenbrandt

University of Gothenburg

Mans Larsson

Eigenvision AB

Olof Enqvist

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Amalie Horstmann Noddeskou-Fink

Odense University Hospital

Oke Gerke

University of Southern Denmark

Odense University Hospital

Poul Flemming Hoilund-Carlsen

Odense University Hospital

University of Southern Denmark

Journal of Nuclear Cardiology

1071-3581 (ISSN) 1532-6551 (eISSN)

Vol. In Press

Subject Categories

Medical Laboratory and Measurements Technologies

Radiology, Nuclear Medicine and Medical Imaging

Medical Image Processing

DOI

10.1007/s12350-021-02649-z

PubMed

33982202

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Latest update

6/1/2021 1