Fully Automatic Volume Segmentation Using Deep Learning Approaches to Assess the Thoracic Aorta, Visceral Abdominal Aorta, and Visceral Vasculature.

Authors

Pouncey AL,Charles E,Bicknell C,Bérard X,Ducasse E,Caradu C

Affiliations (3)

  • Department of Vascular Surgery, Imperial College London, London, UK. Electronic address: [email protected].
  • Department of Vascular Surgery, Imperial College London, London, UK.
  • Bordeaux University Hospital, Department of Vascular Surgery, Bordeaux, France.

Abstract

Computed tomography angiography (CTA) imaging is essential to evaluate and analyse complex abdominal and thoraco-abdominal aortic aneurysms. However, CTA analyses are labour intensive, time consuming, and prone to interphysician variability. Fully automatic volume segmentation (FAVS) using artificial intelligence with deep learning has been validated for infrarenal aorta imaging but requires further testing for thoracic and visceral aorta segmentation. This study assessed FAVS accuracy against physician controlled manual segmentation (PCMS) in the descending thoracic aorta, visceral abdominal aorta, and visceral vasculature. This was a retrospective, multicentre, observational cohort study. Fifty pre-operative CTAs of patients with abdominal aortic aneurysm were randomly selected. Comparisons between FAVS and PCMS and assessment of inter- and intra-observer reliability of PCMS were performed. Volumetric segmentation performance was evaluated using sensitivity, specificity, Dice similarity coefficient (DSC), and Jaccard index (JI). Visceral vessel identification was compared by analysing branchpoint coordinates. Bland-Altman limits of agreement (BA-LoA) were calculated for proximal visceral diameters (excluding duplicate renals). FAVS demonstrated performance comparable with PCMS for volumetric segmentation, with a median DSC of 0.93 (interquartile range [IQR] 0.03), JI of 0.87 (IQR 0.05), sensitivity of 0.99 (IQR 0.01), and specificity of 1.00 (IQR 0.00). These metrics are similar to interphysician comparisons: median DSC 0.93 (IQR 0.07), JI 0.87 (IQR 0.12), sensitivity 0.90 (IQR 0.08), and specificity 1.00 (IQR 0.00). FAVS correctly identified 99.5% (183/184) of visceral vessels. Branchpoint coordinates for FAVS and PCMS were within the limits of CTA spatial resolution (Δx -0.33 [IQR 2.82], Δy 0.61 [IQR 4.85], Δz 2.10 [IQR 4.69] mm). BA-LoA for proximal visceral diameter measurements showed reasonable agreement: FAVS vs. PCMS mean difference -0.11 ± 5.23 mm compared with interphysician variability of 0.03 ± 5.27 mm. FAVS provides accurate, efficient segmentation of the thoracic and visceral aorta, delivering performance comparable with manual segmentation by expert physicians. This technology may enhance clinical workflows for monitoring and planning treatments for complex abdominal and thoraco-abdominal aortic aneurysms.

Topics

Journal Article

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