Abstract
Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain.
Highlights
Building the structural connectome of a brain is widely considered as an essential step of understanding the brain (Seung, 2012)
Thanks to flexible version control in DVID, which allows us to set any checkpoint of proofreading results and create a new version from it, NeuTu is able to visualize body differences from different segmentation versions (Figure 4), which is useful for tracking proofreading progress and for training
The testing results obtained from 27 bodies showed of NeuTu on Mac OS X
Summary
Building the structural connectome of a brain is widely considered as an essential step of understanding the brain (Seung, 2012). Due to the necessity of manual proofreading and its complexity, it is not surprising that various software tools, including Raveler (Olbris et al, 2018), Knossos, Dojo/Mojo (Haehn et al, 2014), Eyewire and VAST (Berger et al, 2018), have been developed almost in parallel for correcting segmentation for dense or sparse reconstructions While they have been successfully applied to produce local connectomes, recent advances in EM imaging (Briggman and Bock, 2012; Eberle et al, 2015; Hayworth et al, 2015; Xu et al, 2017) and segmentation (Beier et al, 2017; Januszewski et al, 2018) suggest new strategies and pose unique challenges for tool design to accelerate proofreading. NeuTu has been used to both densely and sparsely proofread multiple regions of the fly brain, including connectomes of seven columns in medulla (Takemura et al, 2015) and the alpha lobe of the mushroom body (Takemura et al, 2017a)
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