Predicting cardiac indices has long been a focal point in the medical imaging community. While various deep learning models have demonstrated success in quantifying cardiac indices, they remain susceptible to mild input perturbations, e.g., spatial transformations, image distortions, and adversarial attacks. This vulnerability undermines confidence in using learning-based automated systems for diagnosing cardiovascular diseases. In this work, we describe a simple yet effective method to learn robust models for left ventricle (LV) quantification, encompassing cavity and myocardium areas, directional dimensions, and regional wall thicknesses. Our success hinges on employing the biologically inspired steerable pyramid transform (SPT) for fixed front-end processing, which offers three main benefits. First, the basis functions of SPT align with the anatomical structure of LV and the geometric features of the measured indices. Second, SPT facilitates weight sharing across different orientations as a form of parameter regularization and naturally captures the scale variations of LV. Third, the residual highpass subband can be conveniently discarded, promoting robust feature learning. Extensive experiments on the Cardiac-Dig benchmark show that our SPT-augmented model not only achieves reasonable prediction accuracy compared to state-of-the-art methods, but also exhibits significantly improved robustness against input perturbations.

翻译：预测心脏指标长期以来一直是医学影像学界关注的焦点。尽管各种深度学习模型在量化心脏指标方面已取得成功，但它们仍然容易受到轻微输入扰动的影响，例如空间变换、图像畸变和对抗攻击。这种脆弱性削弱了使用基于学习的自动化系统诊断心血管疾病的信心。在本研究中，我们提出了一种简单而有效的方法来学习用于左心室（LV）量化的稳健模型，涵盖心室腔和心肌面积、定向尺寸以及区域壁厚度。我们的成功关键在于采用受生物学启发的可操纵金字塔变换（SPT）进行固定前端处理，该方法具有三个主要优势。首先，SPT的基函数与LV的解剖结构及测量指标的几何特征相吻合。其次，SPT通过参数正则化的形式促进不同方向间的权重共享，并自然地捕捉LV的尺度变化。第三，剩余的高通子带可以方便地被舍弃，从而促进稳健的特征学习。在Cardiac-Dig基准测试上的大量实验表明，与最先进的方法相比，我们基于SPT增强的模型不仅达到了合理的预测精度，而且在面对输入扰动时表现出显著提升的稳健性。