Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Neuroimage. 2010 Dec;53(4):1208-24. doi: 10.1016/j.neuroimage.2010.06.040. Epub 2010 Jun 30.
We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0.4 × 0.5 × 2.0mm(3) resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5 min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel learning-based bias correction technique to achieve excellent agreement with manual segmentation. Initial partitioning of MRI slices into hippocampal 'head', 'body' and 'tail' slices is the only input required from the user, necessitated by the nature of the underlying segmentation protocol. Dice overlap between manual and automatic segmentation is above 0.87 for the larger subfields, CA1 and dentate gyrus, and is competitive with the best results for whole-hippocampus segmentation in the literature. Intraclass correlation of volume measurements in CA1 and dentate gyrus is above 0.89. Overlap in smaller hippocampal subfields is lower in magnitude (0.54 for CA2, 0.62 for CA3, 0.77 for subiculum and 0.79 for entorhinal cortex) but comparable to overlap between manual segmentations by trained human raters. These results support the feasibility of subfield-specific hippocampal morphometry in clinical studies of memory and neurodegenerative disease.
我们提出并评估了一种新方法，用于自动标记海马结构的子字段，这些子字段在常规临床环境中以不到 5 分钟的扫描时间获取，分辨率为 0.4×0.5×2.0mm(3)的 T2 加权磁共振图像。该方法结合了多图谱分割、相似性加权投票和一种新颖的基于学习的偏差校正技术，可实现与手动分割的优异一致性。MRI 切片的初始分区为海马的“头”、“体”和“尾”切片，这是由于底层分割协议的性质，是用户唯一需要的输入。手动和自动分割之间的 Dice 重叠大于 0.87，适用于较大的子字段 CA1 和齿状回，与文献中整个海马体分割的最佳结果相当。在 CA1 和齿状回的体积测量中，组内相关系数大于 0.89。较小的海马子字段的重叠程度较低（CA2 为 0.54，CA3 为 0.62，下托为 0.77，内嗅皮层为 0.79），但与经过训练的人类评估者的手动分割之间的重叠相当。这些结果支持在记忆和神经退行性疾病的临床研究中进行特定子字段的海马形态计量学的可行性。
