Epicardial adipose tissue (EAT) is a type of visceral fat that can secrete large amounts of adipokines to affect the myocardium and coronary arteries. EAT volume and density can be used as independent risk markers measurement of volume by noninvasive magnetic resonance images is the best method of assessing EAT. However, segmenting EAT is challenging due to the low contrast between EAT and pericardial effusion and the presence of motion artifacts. we propose a novel feature latent space multilevel supervision network (SPDNet) with uncertainty-driven and adversarial calibration learning to enhance segmentation for more accurate EAT volume estimation. The network first addresses the blurring of EAT edges due to the medical images in the open medical environments with low quality or out-of-distribution by modeling the uncertainty as a Gaussian distribution in the feature latent space, which using its Bayesian estimation as a regularization constraint to optimize SwinUNETR. Second, an adversarial training strategy is introduced to calibrate the segmentation feature map and consider the multi-scale feature differences between the uncertainty-guided predictive segmentation and the ground truth segmentation, synthesizing the multi-scale adversarial loss directly improves the ability to discriminate the similarity between organizations. Experiments on both the cardiac public MRI dataset (ACDC) and the real-world clinical cohort EAT dataset show that the proposed network outperforms mainstream models, validating that uncertainty-driven and adversarial calibration learning can be used to provide additional information for modeling multi-scale ambiguities.

翻译：心外膜脂肪组织（Epicardial Adipose Tissue, EAT）是一种能分泌大量脂肪因子影响心肌和冠状动脉的内脏脂肪。EAT的体积和密度可作为独立的风险标志物，而通过无创磁共振图像测量体积是评估EAT的最佳方法。然而，由于EAT与心包积液之间的低对比度以及运动伪影的存在，EAT分割极具挑战性。本文提出了一种新颖的特征潜在空间多级监督网络（SPDNet），结合不确定性驱动与对抗校准学习，以提升分割精度，从而实现更准确的EAT体积估计。该网络首先通过将不确定性建模为特征潜在空间中的高斯分布，并利用其贝叶斯估计作为正则化约束来优化SwinUNETR，从而解决因开放医疗环境中低质量或分布外医学图像导致的EAT边缘模糊问题。其次，引入对抗训练策略对分割特征图进行校准，并考虑不确定性引导的预测分割与真实分割之间的多尺度特征差异；通过合成多尺度对抗损失，直接提升判别组织间相似性的能力。在心脏公共MRI数据集（ACDC）和真实临床队列EAT数据集上的实验表明，所提网络优于主流模型，验证了不确定性驱动与对抗校准学习可用于为多尺度模糊性建模提供额外信息。