NeuTu: Software for Collaborative, Large-Scale, Segmentation-Based Connectome Reconstruction.
NeuTu: Software for Collaborative, Large-Scale, Segmentation-Based Connectome Reconstruction.
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DOI：
10.3389/fncir.2018.00101
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发表时间：
2018
影响因子：
3.5
通讯作者：
Plaza SM
中科院分区：
文献类型：
--
作者：
Zhao T;Olbris DJ;Yu Y;Plaza SM
从EM数据集重建连接体通常需要大量的工作来校对自动生成的片段。虽然有许多工具可以实现跟踪或校对，但EM成像和分割质量方面的最新进展为加速校对的工具设计提出了新的策略和独特的挑战。也就是说，我们现在可以访问非常大的多TB EM数据集，其中(1)许多细分市场在很大程度上是正确的，(2)细分市场可能非常大(几个GigaVoxels)，以及(3)预计几个校对人员和科学家将同时合作。在本文中，我们引入NeuTu作为一种解决方案，以在协作环境下高效地校对大量、高质量的分词。NeuTu是我们名为DVID的高性能、可伸缩图像数据库的客户端程序，因此它可以轻松地进行扩展。除了典型校对软件的共同功能外，NeuTu还以其独特的功能驯服了前所未有的海量数据，包括：(1)低延迟的大型可变分割的3D可视化；(2)高度优化的半自动分割的超大型虚假合并的交互式分割；(3)在3D可视化中调查或标记兴趣点的直观用户操作；(4)可视化分割的校对历史；以及(5)基于锁的并发控制的实时协作校对。这些独特的功能使我们能够管理流畅地校对大型数据集的工作流程，而不像其他基于分割的工具那样将它们划分为子集。最重要的是，NeuTu使苍蝇大脑中一些最大的连接体重建和有趣的发现成为可能。
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.
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影响因子：
48
作者：
Hayworth, Kenneth J.;Xu, C. Shan;Lu, Zhiyuan;Knott, Graham W.;Fetter, Richard D.;Tapia, Juan Carlos;Lichtman, Jeff W.;Hess, Harald F.
通讯作者：
Hess, Harald F.
影响因子：
64.8
作者：
Bock, Davi D.;Lee, Wei-Chung Allen;Kerlin, Aaron M.;Andermann, Mark L.;Hood, Greg;Wetzel, Arthur W.;Yurgenson, Sergey;Soucy, Edward R.;Kim, Hyon Suk;Reid, R. Clay
通讯作者：
Reid, R. Clay
影响因子：
48
作者：
Januszewski, Michal;Kornfeld, Joergen;Jain, Viren
通讯作者：
Jain, Viren
影响因子：
3.5
作者：
Berger DR;Seung HS;Lichtman JW
通讯作者：
Lichtman JW
影响因子：
64.8
作者：
通讯作者：
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