Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2444-2450. doi: 10.1109/EMBC46164.2021.9630109.
The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance- Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.
纳米级连接组学领域的研究人员最近已经从数 TB 到数 PB 的密集 3D 神经影像学数据中生成了大脑亚区的自动化和半自动化“布线图”。这个过程涉及到许多具有挑战性和不完善的技术步骤，包括密集 3D 图像分割、各向异性非刚性图像配准和核配准，以及每个神经元及其个体突触连接的像素分类。随着数据量的不断增加，连接组学的生成也越来越普遍，因此科学界能够快速评估连接组产品的质量和准确性就变得尤为重要，这有助于促进数据集的分析和再利用。在这项工作中，我们通过有针对性的查询和大规模图分析，分享了用于评估连接组重建质量的可扩展工具包，并提供了有关如何在未来改进和优化此类连接组校对过程的见解。我们在最近的参考数据集上展示了这些应用和生态系统。临床相关性- 大规模电子显微镜 (EM) 数据提供了一个新颖的机会，可以以前所未有的规模来描述病因和神经疾病及状况。EM 适用于活检等低层次分析；如果克服了某些瓶颈和问题，那么这种增加的规模将为神经网络等领域的研究提供新的可能性。
