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（EAT）是一种内脏脂肪，可分泌大量脂肪因子影响心肌和冠状动脉。EAT体积和密度可作为独立风险标志物，通过无创磁共振成像测量体积是评估EAT的最佳方法。然而，由于EAT与心包积液之间对比度低以及运动伪影的存在，EAT分割具有挑战性。我们提出了一种新颖的特征潜在空间多层次监督网络（SPDNet），结合不确定性驱动与对抗校准学习，以增强分割性能，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，利用其贝叶斯估计作为正则化约束优化SwinUNETR，以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，引入对抗训练策略校准分割特征图，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，合成多尺度对抗损失直接提升组织相似性判别能力。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，所提出网络优于主流模型，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。