Classification of Aortic Shape with Topographical Pair Correlation Functions

TL;DR

This paper introduces a novel topographical pair correlation function (TPCF) to quantify aortic shapes from CT scans, significantly improving classification accuracy for disease detection and surgical outcome prediction.

Contribution

The study presents a new TPCF-based method for analyzing aortic surface features, enhancing the ability to classify disease states from high-dimensional medical imaging data.

Findings

TPCF features achieved 95% classification accuracy.

Shape index parameterization improved disease detection.

TPCF outperformed single-point statistical methods.

Abstract

Quantitative descriptors convert high-dimensional medical images into low-dimensional features capable of differentiating organ shapes that correlate with injury or disease progression for diagnostic purposes. An important example is aortic dissections, which can be imaged using high-resolution CT scans and for which the shape of the true and false lumens of the aorta has long been used to predict disease state and the potential for positive surgical outcomes (namely thoracic endovascular repair or TEVAR). Here we present a method for calculating the topographical pair correlation function (TPCF), a descriptor of the spatial correlation of point estimates for Gaussian curvature, mean curvature, shape index, and bending ratio constrained to the surface of a meshed image. We used the TPCF as a metric to describe aortic shape and extracted quantitative features from the resulting curves. When the TPCF was parameterized by shape index, the area under the curve of the correlation function contributed to a classification accuracy of 0.95 for disease presence and/or impending TEVAR success. Comparison with single-point statistics suggests that the TPCF provides powerful features for classifying the disease state of aortas and more broadly in capturing structural correlations in anatomical data.