Cardiovascular disease classification using radiomics and geometric features from cardiac CT

TL;DR

This paper proposes a clinically interpretable pipeline for cardiovascular disease classification from cardiac CT images, using radiomic and geometric features derived from segmentation and registration, achieving higher accuracy than raw data methods.

Contribution

The work introduces a three-component pipeline that combines segmentation, registration, and feature extraction for improved, interpretable CVD classification from CT images.

Findings

Achieved 87.50% accuracy on the ASOCA dataset.

Outperformed raw image classification with 67.50% accuracy.

Utilized radiomic and deformation features for better interpretability.

Abstract

Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challenge, in this work, we break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification. Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas. These are further utilized to extract clinically interpretable radiomic features as well as deformation field based geometric features (through atlas registration) for CVD classification. Our experiments on the publicly available ASOCA dataset show that utilizing these features leads to better CVD classification accuracy (87.50\%) when compared against classification model trained directly on raw CT images (67.50\%). Our code is publicly available: https://github.com/biomedia-mira/grc-net