Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.
- Visage Imaging, Inc., San Diego, CA, USA.
- Visage Imaging, GmbH, Berlin, Germany.
- Department of Vascular Surgery and Endovascular Therapy, Yale School of Medicine, New Haven, CT, USA.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Vascular Surgery and Endovascular Therapy, Yale School of Medicine, New Haven, CT, USA. [email protected].
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA. [email protected].
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. [email protected].
Abstract
Volumetric assessment of abdominal aortic aneurysms (AAA) offers precise pre- and post-endovascular aortic repair (EVAR) evaluation but is laborious. The primary aim was to train and validate a network facilitating automated segmentation and volume determination of pre- and post-EVAR infrarenal AAAs displayed on computed tomography angiographies (CTA). Secondary aim was evaluation of workflow acceleration. Model was trained on ground truth segmentations. Internal and external validation was performed. AI-generated volumes of total aneurysm, lumen, and thrombus were correlated with ground truth. Model-enabled efficiency gains and semi-automatic AAA segmentations performed by three surgeons were measured. For total aneurysm, mean Dice similarity coefficient was 0.972 ± 0.013 and 0.960 ± 0.035 in internal and external validation. AI-generated thrombus volumes showed a very strong correlation with ground truth in internal (r = 0.996) and external validation (r = 0.940). Mean algorithm-facilitated time savings of 117.1 seconds (56.0%) were demonstrated for total aneurysm. Our institution-agnostic network enables automated volumetric analysis of AAAs.