The scale and resolution of anatomical features extracted from medical CT images are crucial for advancing clinical decision-making tools. While traditional metrics, such as maximum aortic diameter, have long been the standard for classifying aortic diseases, particularly in thoracic aortic dissection, these one-dimensional measures often fall short in capturing the rich geometrical nuances available in progressively advancing imaging modalities. Recent advancements in computational methods and imaging have introduced more sophisticated geometric signatures, but not without increased importance on the anatomical length scales at which they are calculated. Among these, the fluctuation in total integrated Gaussian curvature (δK) over the surface mesh model of the aorta has emerged as a particularly promising metric. However, there exists a critical tradeoff between noise reduction and shape signal preservation within the scale space parameters namely, smoothing intensity, meshing density, and partitioning size. Through a comprehensive analysis of over 1200 unique scale space constructions derived from a cohort of 185 aortic dissection patients, this work pinpoints optimal resolution scales at which shape variations are most strongly correlated with surgical outcomes. Importantly, these findings emphasize the pivotal role of a secondary discretization step, which consistently yielded the most robust signal when scaled to approximately 1 cm. The results presented here not only enhance the interpretability and predictive power of data-driven models but also introduce a methodological framework that integrates statistical reinforcement with domain-specific knowledge to optimize feature extraction across scales. This approach enables the development of models that are not only clinically effective but also inherently resilient to biases introduced by patient population heterogeneity. By focusing on the appropriate intermediate scales for analysis, this study paves the way for more precise and reliable tools in medical imaging, ultimately contributing to improved patient outcomes in cardiovascular surgery.

The authors have declared no competing interest.

This study was funded by the National Institutes of Health, NHLBI, R01HL159205 to LP.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The IRB of the University of Chicago (IRB20-0653, IRB21-0299) gave ethical approval for this work.

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All data produced in the present study are available upon reasonable request to the authors. The imaging data reference in the manuscript is available at https://github.com/SurgBioMech/plos_data

https://github.com/SurgBioMech/plos_data