Wang Lin, Guo Yanrong, Cao Xiaohuan, Wu Guorong, Shen Dinggang
School of Information Science and Technology, Northwest University, Xi'an, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Patch Based Tech Med Imaging (2016). 2016;9993:34-42. doi: 10.1007/978-3-319-47118-1_5. Epub 2016 Sep 22.
In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated group-mean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.
在本文中，我们提出了一种基于多图谱的新型纵向标签融合方法，结合时间稀疏表示技术，以同时分割所有时间点的海马体。首先，我们使用组内纵向配准来同时（1）估计一个受试者图像序列的组平均图像，以及（2）随着时间的推移，将其所有时间点的图像一致地配准到估计出的组平均图像上。然后，通过将所有图谱与组平均图像进行配准，我们可以将所有图谱在纵向上一致地对齐到受试者图像序列的每个时间点。最后，我们提出一种纵向标签融合方法，通过在稀疏表示上施加时间一致性约束，同时标记一组时间上对应的体素，将所有图谱标签传播到受试者图像序列上。实验结果表明，我们提出的方法比现有同类方法能够实现更准确、更一致的海马体分割。
