Vox2Surf: Implicit Surface Reconstruction from Volumetric Data.
Vox2Surf: Implicit Surface Reconstruction from Volumetric Data.
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Vox2Surf：体积数据中隐式表面重建。
DOI：
10.1007/978-3-030-87589-3_66
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发表时间：
2021-09
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体积t1加权和t2加权图像的表面重建是一个耗时的多步骤过程，通常涉及仔细的参数微调，阻碍了基于表面分析的更广泛应用，特别是在大规模研究中。在这项工作中，我们提出了一种基于直接学习连续值有符号距离函数（SDF）作为隐式表面表示的快速表面重建方法。这种连续表示隐式地将曲面的边界编码为零等值面。给定预测的SDF，应用行军立方算法重构目标三维曲面。我们的隐式重建方法同时预测了脑实质、白质和脑顶表面、皮层下结构和脑室的表面。基于Human Connectome Project数据的评估表明，总共22个皮层和皮层下结构的表面重建可以在不到20分钟的时间内完成。
Surface reconstruction from volumetric T1-weighted and T2-weighted images is a time-consuming multi-step process that often involves careful parameter fine-tuning, hindering a more wide-spread utilization of surface-based analysis particularly in large-scale studies. In this work, we propose a fast surface reconstruction method that is based on directly learning a continuous-valued signed distance function (SDF) as implicit surface representation. This continuous representation implicitly encodes the boundary of the surface as the zero isosurface. Given the predicted SDF, the target 3D surface is reconstructed by applying the marching cubes algorithm. Our implicit reconstruction method concurrently predicts the surfaces of the brain parenchyma, the white matter and pial surfaces, the subcortical structures, and the ventricles. Evaluation based on data from the Human Connectome Project indicates that surface reconstruction of a total of 22 cortical and subcortical structures can be completed in less than 20 min.