Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography

Abstract

Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most challenging structures to segment due to its poor contrast boundaries. Instead of solely relying on image gradients, our framework leverages the anatomical context of surrounding anatomical structures to guide the segmentation. This work introduces a novel 3D iterative mesh refinement framework that balances anatomical and geometric forces derived from inherent anatomical rules to refine an initial, possibly ambiguous, segmentation into a high-precision, anatomically plausible result. Designed as a model-agnostic post-processing step, our method uses a 3D vector field to iteratively push the vertices to the correct anatomical locations. Evaluating the refinement on both a high-resolution in-house dataset and a coarse, sparsely annotated open-source dataset, our method consistently improves all volumetric, surface, and anatomical metrics. The framework demonstrates greater improvement when applied to weaker initial segmentations, highlighting its potential for improving segmentations for out-of-domain models and in limited-training-data scenarios. The method is formulated as a gradient-based, GPU-accelerated framework that can be easily extended to other anatomical use cases.