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ToPoMesh:通过拓修改通过CT体积数据精确的3D表面重建

Junjia Chen¹, Qing Zhu¹, Bowen Xie²

¹College of Computer Science, Beijing University Of Technology, Beijing, 100124, China.

Medical & biological engineering & computing

|May 27, 2025

概括

深度学习框架ToPoMesh直接从CT扫描中重建高保真度3D表面网格,克服分辨率限制和文物. 这项创新简化了医疗成像分析,用于手术规划和诊断.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
计算几何学的计算几何学

背景情况:

传统的计算机断层扫描 (CT) 3D重建方法存在分辨率限制和漫长的后处理.
基于voxel的细分和表面重建管道经常引入诸如断开区域和拓不一致的文物.

研究的目的:

引入ToPoMesh,这是一个端到端的深度学习框架,用于从CT体积数据中直接,高准确度的3D表面网格重建.
克服传统方法的局限性,提高3D网格生成的准确性和效率.

主要方法:

开发了一个端到端的深度学习框架,ToPoMesh,用于从CT数据直接进行3D网格重建.
结合了精确的本地和全球形状建模,使用带有剩余连接和自我注意的图形卷积网络.
实施了自适应变异密度 (Avd) 网格分离策略和用于错误修剪和表面光滑的拓修改模块.

主要成果:

与最先进的方法相比,ToPoMesh在多个数据集 (LiTS,MSD胰腺瘤,MSD海马体,MSD) 中表现出优越的性能.
对肝脏数据的Chamfer距离减少了57.4%,F-score与端到端3D重建方法相比提高了0.47%.
定性结果显示,对薄结构和复杂的解剖拓学的增强忠实性,优于细分框架.

结论:

ToPoMesh有效地消除了从CT图像中进行3D网状重建的手动后处理的需要.
该框架可以直接生成3D网格,为外科规划和诊断提供精确的指导.
这种深度学习方法显著提高了基于医学图像的3D建模的准确性和效率.

关键词:

3D表面重建的3D重建一个端到端的框架.图表卷积神经网络的神经网络.拓结构的修改

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