Automatic segmentation of abdominal aortic aneurysms from CT angiography using a context-aware cascaded U-Net

Nan Mu, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Xiaoming Zhang, Todd Rasmussen, Robert McBane, Jingfeng Jiang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

We delineate abdominal aortic aneurysms, including lumen and intraluminal thrombosis (ILT), from contrast-enhanced computed tomography angiography (CTA) data in 70 patients with complete automation. A novel context-aware cascaded U-Net configuration enables automated image segmentation. Notably, auto-context structure, in conjunction with dilated convolutions, anisotropic context module, hierarchical supervision, and a multi-class loss function, are proposed to improve the delineation of ILT in an unbalanced, low-contrast multi-class labeling problem. A quantitative analysis shows that the automated image segmentation produces comparable results with trained human users (e.g., DICE scores of 0.945 and 0.804 for lumen and ILT, respectively). Resultant morphological metrics (e.g., volume, surface area, etc.) are highly correlated to those parameters generated by trained human users. In conclusion, the proposed automated multi-class image segmentation tool has the potential to be further developed as a translational software tool that can be used to improve the clinical management of AAAs.

Original languageEnglish
Article number106569
JournalComputers in Biology and Medicine
Volume158
DOIs
StatePublished - May 2023
Externally publishedYes

Keywords

  • Abdominal aortic aneurysm
  • Context-aware
  • Deep-learning
  • Geometrical analysis
  • Image segmentation
  • Neural network

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