A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension.
A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension.
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用于神经元扩展的新型半自动校对和网格错误检测管道。
DOI：
10.1101/2023.10.20.563359
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wester,Brock
中科院分区：
文献类型：
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作者：
Joyce,Justin;Chalavadi,Rupasri;Chan,Joey;Tanna,Sheel;Xenes,Daniel;Kuo,Nathanael;Rose,Victoria;Matelsky,Jordan;Kitchell,Lindsey;Bishop,Caitlyn;Rivlin,PatriciaK;Villafañe-Delgado,Marisel;Wester,Brock
神经元电子显微镜（EM）数据集的巨大规模和复杂性在数据处理、验证和解释方面提出了重大挑战，需要开发高效、自动化和可扩展的错误检测方法。本文提出了一种利用网格处理技术识别神经元尖端附近潜在错误位置的新方法。尖端的错误检测是一个特别重要的挑战，因为这些错误通常表明许多突触被错误地从它们的母神经元中分离出来，损害了连接组重建的完整性。此外，我们从在半自动校对管道中实现这种错误检测得出了含义和结果。人工校对是目前识别基于机器学习的神经组织分割错误的一种费力、昂贵且必要的方法。这种方法通过系统地突出可能包含不准确的地方，并指导校对人员注意潜在的延续，从而简化了校对过程，加快了错误纠正的速度。
The immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.