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Sökning: id:"swepub:oai:research.chalmers.se:854c47be-697e-4f59-aa45-b52623333234" > Aortic wall segment...

  • Piri, RezaOdense University Hospital,Syddansk Universitet,University of Southern Denmark (författare)

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

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • 2021-05-12
  • Springer Science and Business Media LLC,2022

Nummerbeteckningar

  • LIBRIS-ID:oai:research.chalmers.se:854c47be-697e-4f59-aa45-b52623333234
  • https://research.chalmers.se/publication/524136URI
  • https://doi.org/10.1007/s12350-021-02649-zDOI
  • https://gup.ub.gu.se/publication/308937URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • 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 (författare)
  • Larsson, Mans (författare)
  • Enqvist, Olof,1981Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)enolof (författare)
  • Noddeskou-Fink, Amalie HorstmannOdense University Hospital (författare)
  • Gerke, OkeOdense University Hospital,Syddansk Universitet,University of Southern Denmark (författare)
  • Hoilund-Carlsen, Poul FlemmingSyddansk Universitet,University of Southern Denmark,Odense University Hospital (författare)
  • Odense University HospitalSyddansk Universitet (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Journal of Nuclear Cardiology: Springer Science and Business Media LLC29:4, s. 2001-20101532-65511071-3581

Internetlänk

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