论文标题
在皮质网状分段中传递的模棱两可消息的效用
Utility of Equivariant Message Passing in Cortical Mesh Segmentation
论文作者
论文摘要
在医学图像分析中，皮质区域的自动分割一直是长期以来的挑战。皮质的复杂几何形状通常表示为多边形网格，其分割可以通过基于图的学习方法来解决。当对受试者之间的皮质网格对齐时，当前方法会产生明显较差的分割结果，从而限制了它们处理多域数据的能力。在本文中，我们研究了E（N） - 等级图神经网络（EGNN）的效用，将其性能与平原图神经网络（GNNS）进行了比较。我们的评估表明，由于GNN的能力利用全球坐标系的存在，GNN在对齐网格上的表现要优于对齐网格。在未对准的网格上，普通GNN的性能大大下降，而e（n） - 等级消息传递通过相同的分割结果。也可以通过在重新调整数据（全球坐标系中的共注册网格）上使用普通GNN来获得最佳结果。
The automated segmentation of cortical areas has been a long-standing challenge in medical image analysis. The complex geometry of the cortex is commonly represented as a polygon mesh, whose segmentation can be addressed by graph-based learning methods. When cortical meshes are misaligned across subjects, current methods produce significantly worse segmentation results, limiting their ability to handle multi-domain data. In this paper, we investigate the utility of E(n)-equivariant graph neural networks (EGNNs), comparing their performance against plain graph neural networks (GNNs). Our evaluation shows that GNNs outperform EGNNs on aligned meshes, due to their ability to leverage the presence of a global coordinate system. On misaligned meshes, the performance of plain GNNs drop considerably, while E(n)-equivariant message passing maintains the same segmentation results. The best results can also be obtained by using plain GNNs on realigned data (co-registered meshes in a global coordinate system).
