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bioRxiv · 10.64898/2026.10.11.758402

A robust and scalable image analysis framework for phenotyping human thoracic aorta

Chen, J.·Qian, L.·Gong, R.·Sun, C.·Pham, T.·Martin, C.·Zafar, M.·Elefteriades, J.·Sun, W.·Liang, L.
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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.

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Chen, J., Qian, L., Gong, R., Sun, C., Pham, T., Martin, C., Zafar, M., Elefteriades, J., Sun, W., Liang, L.. 2026-10-13. A robust and scalable image analysis framework for phenotyping human thoracic aorta. https://doi.org/10.64898/2026.10.11.758402

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