AI 中文总结
针对心脏CT扫描心包分割难题，提出解剖学感知3D网格细化框架，利用解剖和几何力平衡，通过3D向量场迭代细化分割，在多数据集上提升指标，尤其适用于弱初始分割及相关场景，且可扩展。
AI 中文摘要
在心脏CT扫描中准确描绘心包对于量化心外膜脂肪组织至关重要，但由于其对比度边界较差，它仍然是最难分割的结构之一。我们的框架并非仅依赖图像梯度，而是利用周围解剖结构的解剖学背景来指导分割。本文介绍了一种新颖的3D迭代网格细化框架，该框架平衡了源自固有解剖规则的解剖学和几何力，将初始的、可能模糊的分割细化为高精度、符合解剖学的结果。作为一个与模型无关的后处理步骤，我们的方法使用3D向量场将顶点迭代地推到正确的解剖位置。在高分辨率内部数据集和粗糙、注释稀疏的开源数据集上评估细化效果时，我们的方法持续改善了所有体积、表面和解剖学指标。当应用于较弱的初始分割时，该框架显示出更大的改进，突出了其在改善域外模型和有限训练数据场景下分割的潜力。该方法被制定为基于梯度的、GPU加速的框架，可轻松扩展到其他解剖学用例。
英文摘要
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.
CommentsThis preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in the proceedings for MIUA 2026
