VICE: Visual Identification and Correction of Neural Circuit Errors

TL;DR

This paper introduces VICE, a framework that enhances the efficiency of proofreading neural circuit reconstructions by automating error detection and providing interactive 3D visualizations, thereby streamlining the correction of connectivity errors in EM datasets.

Contribution

The paper presents a novel analytics framework that automates error detection and visualization to improve proofreading of neural connectivity graphs at the single-synapse level.

Findings

Users found the framework more efficient for proofreading tasks.

The framework improved understanding of neural connectivity graphs.

Participants provided positive subjective feedback on usability.

Abstract

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.