Automatic branch detection of the arterial system from abdominal aortic segmentation

Sébastien Riffaud (1) , Gwladys Ravon (1) , Thibault Allard (2) , Florian Bernard (2) , Angelo Iollo (1) , Caroline Caradu (3)

1 MEMPHIS - Modeling Enablers for Multi-PHysics and InteractionS
2 Nurea
3 CHU Bordeaux

We present a new method to automatically identify the different arteries present in an abdominal aortic segmentation. In this approach, the arterial system is first represented by a vascular tree, extracted from the segmentation and containing the topologic and geometric features (branch position, branch direction, branch length, branch diameter) of the arterial system. Then, the branches of the vascular tree are matched with the main arteries origi- nating from the aorta: celiac artery, superior mesenteric artery, renal arteries and common iliac arteries. This match is determined by maximizing a similarity measure between the dif- ferent branches and corresponding arteries. We evaluate this method on 239 segmentations obtained from 102 different patients. The results demonstrate the accuracy of the proposed method, capable of delivering an error of less than 2.5% for the identification of the celiac and superior mesenteric arteries, 8.4% for the renal arteries, and 2.1% for the common iliac arteries.

Mots clés: Computed tomography; Abdominal aortic aneurysm; Automatic branch detection; Graph matching method; Aortic root

Domaines: Cardiologie et système cardiovasculaire; Imagerie médicale; Combinatoire [math.CO]

Sébastien Riffaud, Gwladys Ravon, Thibault Allard, Florian Bernard, Angelo Iollo, et al.. Automatic branch detection of the arterial system from abdominal aortic segmentation. Medical and Biological Engineering and Computing, 2022, ⟨10.1007/s11517-022-02603-2⟩. ⟨hal-03520790v2⟩