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.

中文翻译：

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VICE：神经回路错误的视觉识别和校正

单个突触分辨率下的神经元连接图为科学家们提供了一种工具来了解健康和疾病中的神经系统。大脑电子显微镜 (EM) 数据集中的自动图像分割和突触预测的最新进展使得在纳米尺度上重建神经元成为可能。然而，自动分割有时难以正确分割大神经元，需要人工来校对其输出。一般校对涉及检查大量数据以纠正像素级别的分割错误，这是一个视觉密集且耗时的过程。本文介绍了简化校对的分析框架的设计和实施，重点关注与连接相关的错误。我们通过自动化的可能错误检测和突触聚类来实现这一点，通过高度交互的 3D 可视化驱动校对工作。特别是，我们的策略集中在校对单个单元的本地电路以确保基本的完整性水平。我们通过用户研究证明了我们框架的实用性，并报告了用户的定量和主观反馈。总体而言，用户发现该框架在校对、理解不断变化的图形和共享纠错策略方面更有效。s 效用与用户研究并报告来自我们用户的定量和主观反馈。总体而言，用户发现该框架在校对、理解不断变化的图形和共享纠错策略方面更有效。s 效用与用户研究并报告来自我们用户的定量和主观反馈。总体而言，用户发现该框架在校对、理解不断变化的图形和共享纠错策略方面更有效。