Advancing morphometric assessment of the aorta and left ventricle from dynamic CT: a deep learning–based study

Abstract Aims Morphometric analysis of the thoracic aorta (TA) and left ventricle (LV) plays a fundamental role in detecting anatomical abnormalities and functional alterations to support preoperative planning, predict disease risk and inform device design. However, conventional approaches to morphometric evaluation are typically performed manually using visualization software, thus resulting in time-consuming, operator-dependent processes that are usually limited to static imaging. This work presents an automated three-dimensional image-based methodological framework for dynamic morphometric analysis, from ECG-gated CT datasets. Methods and results A multi-label 3D U-Net was trained for the automatic segmentation of the TA and LV using a dataset of 50 single-phase CT scans, with ground-truth labelmaps validated under expert radiological supervision. Model performance was tested on an independent multi-phase cohort of 10 patients. The network achieved high segmentation accuracy, with Dice scores of 97.77 ± 0.31% for the TA and 91.45 ± 1.26% for the LV on the multi-phase test set. The resulting 3D surface models enabled the computation of geometric descriptors, including volumetric indices, displacement fields, and centerline-based diameters, across cardiac phases on 42 patients. Overall, the framework demonstrated robustness to variations in contrast intensity, cardiac motion, and inter-patient anatomical variability, providing a reliable and reproducible pipeline for comprehensive, three-dimensional, and time-resolved morphometric analysis of the ventriculo-arterial complex with physiological or mildly altered anatomy. Conclusion This approach has strong potential for future clinical translation, supporting quantitative assessment of cardiac function and aortic pathophysiology.