NeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects

Abstract

In the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users. To tackle the associated challenges, this paper presents NeuroBlocks, which is a novel visualization system for tracking the state, progress, and evolution of very large volumetric segmentation data in neuroscience. NeuroBlocks is a multi-user web-based application that seamlessly integrates the diverse set of tools that neuroscientists currently use for manual and semi-automatic segmentation, proofreading, visualization, and analysis. NeuroBlocks is the first system that integrates this heterogeneous tool set, providing crucial support for the management, provenance, accountability, and auditing of large-scale segmentations. We describe the design of NeuroBlocks, starting with an analysis of the domain-specific tasks, their inherent challenges, and our subsequent task abstraction and visual representation. We demonstrate the utility of our design based on two case studies that focus on different user roles and their respective requirements for performing and tracking the progress of segmentation and proofreading in a large real-world connectomics project.

Scalable Interactive Visualization for Connectomics

“…Proofreading: All available segmentation methods make mistakes [10], and so the results must be proofread by humans before any biological analysis occurs [11][12][13]. For proofreading, intelligent interactive visualization tools are key to minimizing the time committed.…”

“…For experts, Catmaid [29] and the Viking Viewer [30] are collaborative annotation frameworks which allow users to create skeleton segmentations for large data sets. More recently, Neuroblocks [13] proposed an online-system for tracking the progress and evolution of a large-scale segmentation project.…”

“…), a Web-based proofreading application that supports multiple users. We evaluated Dojo and other proofreading software with novices trained by experts as part of a quantitative user study (between-subjects experiment) [11], and designed mechanisms for quality control [13].…”

RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphs

“…13–17 In this work, we design a neural encoding inspired by the Swordplots of Ma et al 4 However, to the best of our knowledge, no other visualizations exist for multi-scale biological connectivity data.…”

“…In neuroscience studies, an overlay approach 8,19,20 is commonly used when the non-spatial feature represents only functional connections. However, as the non-spatial data becomes more complex (activation levels, connectivity, clusters, dynamic characteristics, and other statistics), the linked-view paradigm 15–17,21 becomes the default choice. Nowke et al 14 use a hybrid approach that consists of both overlays and linked views.…”