从MRI重建和分割皮质表面对于广泛的大脑分析至关重要。然而,大多数方法都遵循多步缓慢的过程,例如连续的球形膨胀和配准,这需要相当多的计算时间。为了克服这些多步骤产生的限制,我们提议SegRecon,一种集成的端到端深度学习方法,可以在一个步骤中直接从MRI体积中联合重建和分割皮质表面。我们训练一个基于体积的神经网络来预测,对于每个体素,到多个嵌套曲面的符号距离及其在图集空间中的相应球面表示。这是,例如,可用于联合重建和分割白色至灰质界面和灰质至CSF(pial)表面。我们通过在MindBoggle上进行的一组全面的实验来评估我们的表面重建和分割方法的性能,ABIDE和OASIS数据集。就Hausdorff到FreeSurfer生成的表面的平均距离而言,我们的重建误差小于0.52mm和0.97mm。同样,分组结果显示,相对于FreeSurfer,平均骰子提高了4%以上,除了观察到在标准桌面工作站上从数小时到数秒的计算速度急剧加快之外。
Reconstructing and segmenting cortical surfaces from MRI is essential to a wide range of brain analyses. However, most approaches follow a multi-step slow process, such as a sequential spherical inflation and registration, which requires considerable computation times. To overcome the limitations arising from these multi-steps, we propose SegRecon, an integrated end-to-end deep learning method to jointly reconstruct and segment cortical surfaces directly from an MRI volume in one single step. We train a volume-based neural network to predict, for each voxel, the signed distances to multiple nested surfaces and their corresponding spherical representation in atlas space. This is, for instance, useful for jointly reconstructing and segmenting the white-to-gray-matter interface and the gray-matter-to-CSF (pial) surface. We evaluate the performance of our surface reconstruction and segmentation method with a comprehensive set of experiments on the MindBoggle, ABIDE and OASIS datasets. Our reconstruction error is found to be less than 0.52 mm and 0.97 mm in terms of average Hausdorff distance to the FreeSurfer generated surfaces. Likewise, the parcellation results show over 4% improvements in average Dice with respect to FreeSurfer, in addition to an observed drastic speed-up from hours to seconds of computation on a standard desktop station.