Cardiovascular disease classification using radiomics and geometric features from cardiac CT

• URL: http://arxiv.org/abs/2506.22226v1
• Date: Fri, 27 Jun 2025 13:43:05 GMT
• Title: Cardiovascular disease classification using radiomics and geometric features from cardiac CT
• Authors: Ajay Mittal, Raghav Mehta, Omar Todd, Philipp Seeböck, Georg Langs, Ben Glocker,
• Abstract summary: We break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification.<n>Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas.<n>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%)
• Score: 14.254217534681997
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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