Editor's Choice - Fully Automatic Volume Segmentation Using Deep Learning Approaches to Assess the Thoracic Aorta,

Anna-Louise Pouncey1, Edmund Charles1, Colin Bicknell1

  • 1Department of Vascular Surgery, Imperial College London, London, UK.

Insights

Fully automatic volume segmentation (FAVS) using AI accurately segments thoracic and visceral aortas, matching physician performance. This AI technology can improve workflows for complex aortic aneurysm treatment planning and monitoring.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Vascular Surgery

Background:

  • Computed tomography angiography (CTA) is crucial for analyzing complex aortic aneurysms but is labor-intensive and variable.
  • Current AI-driven segmentation is validated for infrarenal aorta but needs testing for thoracic and visceral segments.

Purpose of the Study:

  • To assess the accuracy of fully automatic volume segmentation (FAVS) against physician-controlled manual segmentation (PCMS).
  • To evaluate FAVS performance in the descending thoracic aorta, visceral abdominal aorta, and visceral vasculature.

Main Methods:

  • Retrospective, multicenter observational cohort study of 50 pre-operative CTAs.
  • Comparison of FAVS and PCMS using sensitivity, specificity, Dice similarity coefficient (DSC), and Jaccard index (JI).
  • Analysis of visceral vessel identification and proximal visceral diameter measurements.

Main Results:

  • FAVS showed comparable volumetric segmentation performance to PCMS (median DSC 0.93, JI 0.87).
  • FAVS correctly identified 99.5% of visceral vessels with branchpoint coordinates within CTA spatial resolution limits.
  • Diameter measurements by FAVS agreed well with PCMS and interphysician variability.

Conclusions:

  • FAVS offers accurate and efficient segmentation of thoracic and visceral aortas, comparable to expert physicians.
  • This AI technology has the potential to enhance clinical workflows for managing complex aortic aneurysms.
Abstract

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