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

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.

---

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