Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System

Fabien Lareyre; Caroline Caradu; Arindam Chaudhuri; Cong Duy Lê; Gilles Di Lorenzo; Cédric Adam; Marion Carrier; Juliette Raffort; Study Group Collaborators

doi:10.1016/j.ejvsvf.2023.05.001

Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System

EJVES Vasc Forum. 2023 May 6:59:15-19. doi: 10.1016/j.ejvsvf.2023.05.001. eCollection 2023.

Authors

Collaborators

Study Group Collaborators:
Raphaël Coscas⁸, Jérémie Jayet⁸, Raphaël Soler⁹, Lucie Salomon du Mont¹⁰

Affiliations

¹ Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France.
² Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France.
³ Department of Vascular Surgery, University Hospital of Bordeaux, France.
⁴ Bedfordshire - Milton Keynes Vascular Centre, Bedford Hospital NHS Trust, Bedford, UK.
⁵ Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, France.
⁶ Institute 3IA Côte d'Azur, Université Côte d'Azur, France.
⁷ Clinical Chemistry Laboratory, University Hospital of Nice, France.
⁸ Department of Vascular Surgery, Ambroise Paré University Hospital, AP-HP, Boulogne-Billancourt, France.
⁹ Department of Vascular and Endovascular Surgery, Hôpital Saint Joseph, Marseille, France.
¹⁰ Department of Vascular Surgery, University Hospital of Besançon, France.

Abstract

Introduction: Visceral arterial aneurysms (VAAs) are life threatening. Due to the paucity of symptoms and rarity of the disease, VAAs are underdiagnosed and underestimated. Artificial intelligence (AI) offers new insights into segmentation of the vascular system, and opportunities to better detect VAAs. This pilot study aimed to develop an AI based method to automatically detect VAAs from computed tomography angiography (CTA).

Methods: A hybrid method combining a feature based expert system with a supervised deep learning algorithm (convolutional neural network) was used to enable fully automatic segmentation of the abdominal vascular tree. Centrelines were built and reference diameters of each visceral artery were calculated. An abnormal dilatation (VAAs) was defined as a substantial increase in diameter at the pixel of interest compared with the mean diameter of the reference portion. The automatic software provided 3D rendered images with a flag on the identified VAA areas. The performance of the method was tested in a dataset of 33 CTA scans and compared with the ground truth provided by two human experts.

Results: Forty-three VAAs were identified by human experts (32 in the coeliac trunk branches, eight in the superior mesenteric artery, one in the left renal, and two in the right renal arteries). The automatic system accurately detected 40 of the 43 VAAs, with a sensitivity of 0.93 and a positive predictive value of 0.51. The mean number of flag areas per CTA was 3.5 ± 1.5 and they could be reviewed and checked by a human expert in less than 30 seconds per CTA.

Conclusion: Although the specificity needs to be improved, this study demonstrates the potential of an AI based automatic method to develop new tools to improve screening and detection of VAAs by automatically attracting clinicians' attention to suspicious dilatations of the visceral arteries.

Keywords: Artificial intelligence; Automated segmentation; Machine learning; Vascular; Visceral arterial aneurysm.