A fast graph-based algorithm for automated segmentation of subcutaneous and visceral adipose tissue in 3D abdominal computed tomography images

• Autorzy: Kucybala Iwona , Tabor Zbislaw , Ciuk Szymon , Chrzan Robert , Urbanik Andrzej , Wojciechowski Wadim

Identyfikatory

DOI

10.1016/j.bbe.2020.02.009

• Języki publikacji: EN

Abstrakty

EN

The aim of the study was to create an accurate method of automated subcutaneous (SAT) and visceral (VAT) adipose tissue detection basing on three-dimensional (3D) computed tomography (CT) scans. One hundred and forty abdominal CT examinations were analysed. An algorithm for automated detection of SAT and VAT consisted of following steps: thresholding of an analysed image, detection of a patient's body region, separation of SAT and VAT. The algorithm was sequentially applied to each 2D axial slice of a 3D examination. To assess the accuracy of the proposed method, automated and manual segmentations (performed by two readers) of SAT and VAT were compared using Dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Mean DSC was equal to 99.6% ± 0.4% for SAT and 99.6% ± 0.5% for VAT, which was equal to DSC obtained for comparison between both readers. In 90% of cases DSC was equal or above 99.0% and the minimal DSC was 97.6%. AHD equalled to 0.04 ± 0.06 for SAT and 0.13 ± 0.23 for VAT (automated vs. manual segmentations), while AHD for comparison of two manual segmentations was 0.03 ± 0.07 for SAT and 0.09 ± 0.20 for VAT. The processing time for a single slice was 0.16 s for an automated segmentation and 510 min for a manual segmen- tation. The processing time of an entire 3D stack (around 40 2D slices) was on average 6.5 s. Our algorithm for the automated detection of SAT and VAT on 3D CT scans has the same accuracy as manual segmentation and performs equally well for both adipose tissue compartments.

Słowa kluczowe

EN

abdominal adipose tissue; visceral adipose tissue; subcutaneous adipose tissue; obesity; automated detection; image segmentation; computed tomography

PL

tkanka tłuszczowa; wykrywanie automatyczne; segmentacja obrazu; tomografia komputerowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 729--739
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Kucybala Iwona

• Department of Radiology, Jagiellonian University Medical College, Krakow, Poland

autor

Tabor Zbislaw

• AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30-059 Kraków, Poland

autor

Ciuk Szymon

• Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wroclaw, Poland

autor

Chrzan Robert

• Department of Radiology, Jagiellonian University Medical College, Krakow, Poland

autor

Urbanik Andrzej

• Department of Radiology, Jagiellonian University Medical College, Krakow, Poland

autor

Wojciechowski Wadim

• Department of Radiology, Jagiellonian University Medical College, Krakow, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).