Max Planck Institute for Medical Research, Heidelberg, Germany.
Nat Neurosci. 2011 Jul 10;14(8):1081-8. doi: 10.1038/nn.2868.
Neuroanatomic analysis depends on the reconstruction of complete cell shapes. High-throughput reconstruction of neural circuits, or connectomics, using volume electron microscopy requires dense staining of all cells, which leads even experts to make annotation errors. Currently, reconstruction speed rather than acquisition speed limits the determination of neural wiring diagrams. We developed a method for fast and reliable reconstruction of densely labeled data sets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a visualization-annotation software tool called KNOSSOS, is ∼50-fold faster than volume labeling. Errors are detected and eliminated by a redundant-skeleton consensus procedure (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the reliability of consensus skeletons. Focused reannotation of difficult locations promises a rather steep increase of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets.
神经解剖分析取决于完整细胞形状的重建。使用体积电子显微镜对神经回路（或连接组学）进行高通量重建需要对所有细胞进行密集染色，这甚至会导致专家产生注释错误。目前，重建速度而不是采集速度限制了神经布线图的确定。我们开发了一种快速可靠的重建密集标记数据集的方法。我们的方法基于使用称为 KNOSSOS 的可视化注释软件工具多次手动对每个神经突进行骨架化（多次），与体积标记相比，速度快约 50 倍。通过冗余骨架共识程序（RE-SCOP）检测和消除错误，该程序使用真实神经突连接如何转化为注释决策的统计模型。RE-SCOP 还估计共识骨架的可靠性。对困难位置的重点重新注释有望随着平均骨架冗余度的增加而大幅提高可靠性，从而几乎可以对大型神经解剖数据集进行无错误分析。
