AortaPlot: A Modular End-to-End Protocol for Automated Aortic Imaging Analysis, Visualization, and Standardized Clinical Reporting to Support Aortic Surveillance

Abstract Background: Quantitative assessment of aortic morphology from computed tomography (CT) underpins diagnosis, surveillance, and risk stratification in aortic disease. However, existing analysis workflows are often fragmented, algorithm-specific, and limited to discrete vessel segments, constraining reproducibility, longitudinal comparison, and integration of emerging computational methods. The objective of this study was to describe AortaPlot, a modular, end-to-end computational framework designed to standardize continuous aortic quantification and generate anatomically contextualized outputs suitable for clinical interpretation, as well as downstream machine learning and longitudinal modeling. Methods: AortaPlot ingests CT imaging data and performs three-dimensional aortic segmentation using a replaceable segmentation engine. A continuous vascular centerline is computed from the aortic root to the aortic bifurcation and parameterized by arc length. Orthogonal cross-sectional planes are resampled at configurable intervals, and vessel diameter is quantified using a geometry-adaptive Sliding Axis algorithm that identifies the maximum orthogonal diameter independent of cross-sectional shape. Twenty anatomical landmarks are automatically detected and embedded within the measurement framework. The system generates landmark-annotated diameter-versus-length profiles, high-resolution regional analyses, standardized radiomic feature sets, annotated full length aortic visualization and fixed-view multi-angle 3D surface visualizations. Results: Across representative normal and pathological cases, AortaPlot reproducibly reconstructed full-length centerlines and generated anatomically ordered, landmark-annotated diameter profiles spanning the entire aorta. Continuous profiling captured physiologic tapering as well as focal aneurysmal dilation, while region-specific oversampling enabled detailed characterization of localized pathology. Outputs are compiled into standardized, surgeon-oriented reports integrating continuous quantitative profiles, focused segment analysis, anatomically contextualized radiomic features, landmark measurements, annotated aortic visualization and six-view 3D visualizations with fixed rotational spacing. Conclusions: AortaPlot establishes a reproducible, extensible framework for continuous aortic morphology analysis that unifies segmentation, measurement, anatomical context, and visualization within a single pipeline. By standardizing representation across the full vessel length and across time, this protocol reduces analytic variability and produces structured outputs directly amenable to radiomics, machine learning, and longitudinal modeling, providing a scalable foundation for reproducible aortic surveillance and future AI-driven risk stratification.