Year: 2026
Type: preprint
Abstract: Large-scale phenotyping of the thoracic aorta requires a workflow in which every step, from segmentation to phenotype extraction, runs without manual intervention. We present a robust and scalable framework that converts contrast-enhanced CT into anatomical landmarks, a patient-specific quadrilateral mesh, a wall-stress field and regional phenotypes of the thoracic aorta. Four nnU-Net segmentation tasks delineate the aorta, its anatomical regions and two partitions of the aortic root. A surface–curve–point procedure derives the annulus, the sinotubular junction, the ostia of the supra-aortic branches, the cusp nadirs and the commissures from the interfaces between segmented regions. We fit a structured template of 24,882 quadrilateral elements to the segmented aortic surface by staged, differentiable mesh deformation and compute the transmural mean wall stress with PyTorch-FEA. The structured mesh provides node-to-node correspondence across patients, anchored to the anatomy by the landmarks, from which the framework extracts regional morphological and biomechanical phenotypes.
Institutions: University of Miami, Yale New Haven Hospital, Yale University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221682091