Mask-HybridGNet：基于图的分割与从像素级监督中涌现的解剖对应关系
Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision
AI总结：
Mask-HybridGNet通过像素级监督训练图模型，实现无需人工标注的解剖结构分割，并生成稳定的解剖图谱。
摘要：
基于图的医学图像分割利用边界图来表示解剖结构，提供固定拓扑的地标和固有的群体级对应关系。然而，其临床应用受到一个主要限制：训练数据集中的手动标注地标，这些地标在患者之间保持点对点对应关系的很少见。我们引入了Mask-HybridGNet框架，该框架直接使用标准像素级掩码训练图模型，消除了对人工地标标注的需求。我们的方法通过结合Chamfer距离监督和基于边的正则化，将可变长度的真实边界与固定长度的地标预测对齐，以确保局部平滑和规则的地标分布，并通过可微化的栅格化进一步优化。该框架的一个显著特性是，预测的地标位置在患者之间变得一致地与特定解剖位置相关联，而无需显式的对应监督。这种隐式的图谱学习使时间追踪、跨切片重建和形态学群体分析成为可能。除了直接分割外，Mask-HybridGNet可以从现有分割掩码中提取对应关系，使其能够从任何高质量像素级模型生成稳定的解剖图谱。在胸片、心脏超声、心脏MRI和胎儿影像上的实验表明，我们的模型在与最先进的像素级方法竞争的同时，通过固定图邻接矩阵强制边界连通性来确保解剖合理性。该框架利用标准分割掩码的广泛可用性，构建了保持拓扑完整性的结构模型，并提供了隐含的对应关系。
英文摘要：
Graph-based medical image segmentation represents anatomical structures using boundary graphs, providing fixed-topology landmarks and inherent population-level correspondences. However, their clinical adoption has been hindered by a major requirement: training datasets with manually annotated landmarks that maintain point-to-point correspondences across patients rarely exist in practice. We introduce Mask-HybridGNet, a framework that trains graph-based models directly using standard pixel-wise masks, eliminating the need for manual landmark annotations. Our approach aligns variable-length ground truth boundaries with fixed-length landmark predictions by combining Chamfer distance supervision and edge-based regularization to ensure local smoothness and regular landmark distribution, further refined via differentiable rasterization. A significant emergent property of this framework is that predicted landmark positions become consistently associated with specific anatomical locations across patients without explicit correspondence supervision. This implicit atlas learning enables temporal tracking, cross-slice reconstruction, and morphological population analyses. Beyond direct segmentation, Mask-HybridGNet can extract correspondences from existing segmentation masks, allowing it to generate stable anatomical atlases from any high-quality pixel-based model. Experiments across chest radiography, cardiac ultrasound, cardiac MRI, and fetal imaging demonstrate that our model achieves competitive results against state-of-the-art pixel-based methods, while ensuring anatomical plausibility by enforcing boundary connectivity through a fixed graph adjacency matrix. This framework leverages the vast availability of standard segmentation masks to build structured models that maintain topological integrity and provide implicit correspondences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
