用解剖结构和形变特征提升心脏病CT分类准确率
Cardiovascular disease classification using radiomics and geometric features from cardiac CT
- 先分割解剖结构，再配准生成健康模板，提取可解释特征
- 在ASOCA数据集上分类准确率达87.5%，远超直接用原始图像的67.5%
- 适合关注临床可解释性的医疗影像分析研究者
从计算机断层扫描（CT）图像中自动检测和分类心血管疾病（CVD）对辅助临床决策至关重要。然而，现有深度学习方法大多直接处理原始CT数据，或结合心脏解剖结构分割进行端到端分类，导致结果难以从临床角度解释。为此，本文将CVD分类流程分解为三个环节：（i）图像分割，（ii）图像配准，（iii）下游分类。具体地，采用Atlas-ISTN框架与最新分割基础模型生成解剖结构分割图及正常健康模板。进一步利用这些结果提取临床可解释的放射组学特征和基于配准的形变场几何特征，用于CVD分类。在公开的ASOCA数据集上的实验表明，使用这些特征可使分类准确率达到87.50%，显著优于直接基于原始CT图像训练的模型（67.50%）。代码已开源：https://github.com/biomedia-mira/grc-net
原文摘要 · Abstract (English)
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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