PDF(Pubmed)
Abstract：
Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.
摘要：
纵向海马萎缩通常用作辅助临床诊断痴呆的进行性标志物。然而,萎缩的精确量化受到跨多个独立扫描的MRI伪影导致的纵向分割误差的限制.为了从纵向3TT1加权MR图像中准确分割海马形态，我们提出了一种称为GeoLongSeg的不同形态测地指导深度学习方法，以通过增强个体内部形态一致性来减轻与疾病无关的纵向变异性。具体来说,我们整合了测地形状回归，估计解剖形状的平滑变形过程的进化模型，成一个两阶段的分割网络。我们在第一阶段网络中采用3DU-Net，并具有增强的注意力机制以进行独立分割。然后，通过测地线形状回归估计海马形状的进化轨迹，并将其送入第二网络以完善独立分割。我们验证了GeoLongSeg在通过测试重测可靠性评估的纵向形态一致性方面优于其他四个最先进的分割管道，方差比和萎缩轨迹。在评估阿尔茨海默病神经影像学倡议（ADNI）纵向数据中的海马萎缩时，基于GeoLongSeg的结果表现出痴呆患者双侧海马的空间和时间局部萎缩。与其他方法的结果相比，从GeoLongSeg分割得出的这些特征在区分患者和正常对照方面表现出最大的区分能力。总的来说,GeoLongSeg为从纵向MR图像中提取海马形态提供了一个准确有效的分割网络，这有助于在痴呆症早期对海马体进行精确的萎缩测量。
