Fig. 1: Proofreading with Dojo. We present a web-based application for interactive proofreading of automatic segmentations of connectome data acquired via electron microscopy. Split, merge and adjust functionality enables multiple users to correct the labeling of neurons in a collaborative fashion. Color-coded structures can be explored in 2D and 3D. Abstract—Proofreading refers to the manual correction of automatic segmentations of image data. In connectomics, electron mi-croscopy data is acquired at nanometer-scale resolution and results in very large image volumes of brain tissue that require fully automatic segmentation algorithms to identify cell boundaries. However, these algorithms require hundreds of corrections per cubic micron of tissue. Even though this task is time consuming, it is fairly easy for humans to perform corrections through splitting, merging, and adjusting segments during proofreading. In this paper we present the design and implementation of Mojo, a fully-featured single-user desktop application for proofreading, and Dojo, a multi-user web-based application for collaborative proofreading. We evaluate the accuracy and speed of Mojo, Dojo, and Raveler, a proofreading tool from Janelia Farm, through a quantitative user study. We designed a between-subjects experiment and asked non-experts to proofread neurons in a publicly available connectomics dataset. Our results show a significant improvement of corrections using web-based Dojo, when given the same amount of time. In addition, all participants using Dojo reported better usability. We discuss our findings and provide an analysis of requirements for designing visual proofreading software. Index Terms—Proofreading, Segmentation, Connectomics, Quantitative Evaluation 1

Fig. 1: NeuroLines neurite visualization. We abstract the original 3D structure and topology of neurites segmented in nanoscale brain tissue data into a 2D subway map visualization that preserves topology and relative distances. Left: Volume rendering of a dendrite (red) and connected axons (blue). Right: NeuroLines abstraction of the same data, represented as subway lines to more clearly show branches, clusters of adjacent synapses, individual synapses, and the actual connections (shown on demand). Abstract—We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data. Index Terms—Connectomics, Neuroscience, Data Abstraction, Multi-Trees, Focus+Context. 1