A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework's utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
翻译:在单一突触的解析中,神经神经元连接图向科学家提供了了解神经系统健康和疾病的工具。大脑电子显微镜(EM)数据集中自动图像分解和突触预测的最近进展使得在纳米尺度上重建神经元成为可能。然而,自动分解有时很难正确地分解大型神经元,需要人类努力校正其输出。一般校对需要检查大量数量,以纠正像素层次的分解错误,这是一个视觉密集和耗时的过程。本文介绍一个简化校对的分析性框架的设计和实施,侧重于与连接有关的错误。我们通过自动的可能的神经分解和突触集来完成这项工作,以高度互动的3D分解驱动校正努力。特别是,我们的战略中心是校准单细胞的本地电路,以确保基本的完整性。我们通过用户研究来展示我们的框架的效用,并报告用户的数量和主观反馈。总体而言,用户发现框架对于校正错误、理解演进的图表战略、理解演进式和图表的更有效率。