Analysis of paracardial adipose tissues using deep learning segmentation in CT calcium score images

Epicardial (EAT) and paracardial (PAT) adipose tissues (inside and outside the pericardial sac, respectively) are thought to be associated with major adverse cardiovascular events (MACE). Our long-term goal is to include PAT and EAT in a comprehensive survival analysis of MACE. Here we developed an automated method for segmenting PAT in computed tomography calcium score (CTCS) scans. Analysts identified the top and bottom heart slices by anatomical evidence, and segmented PAT in a slice-by-slice basis. Our proposed PAT segmentation approach (DeepPAT) used preprocessing steps and a multi-class automated semantic segmentation (DeepLab-v3plus) network. Preprocessing steps incorporated filtering to reduce noise, window-leveling to draw attention to sac, and morphological operations to close gaps within mask volumes. DeepPAT was trained/tested on (30/22) CTCS scans from the University Hospitals of Cleveland. The output mask voxels were classified as either enclosed sac, PAT, or background. PAT region is further thresholded with standard fat HU range [-190, -30]. The DeepPAT showed excellent segmentation compared to ground truth (manual) with an average Dice score (82.5%±3.93) and correlation of (R=99.23%, P<<0.001). PAT volume difference was (4.08%±7.78) while the PAT mean HU value changed (2.65%±4.72). The EAT and PAT volumes had a noticeable correlation R=82.9% (P<<0.001). Volumes for MACE/no-MACE (5/17 patients) subgroups showed significance for PAT (P= 0.023), while EAT had better significance (P=0.004). Mean HU values showed less significance in both PAT (p=0.81) and EAT (p=0.18). Our research results offer valuable insights that can be utilized for cardiovascular risk assessment studies.