  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.
