Memory and Time Efficient 3D Neuron Morphology Tracing in Large-Scale Images

Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Rong Gao, Hanchuan Peng, Weidong (Tom) Cai
{"title":"Memory and Time Efficient 3D Neuron Morphology Tracing in Large-Scale Images","authors":"Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Rong Gao, Hanchuan Peng, Weidong (Tom) Cai","doi":"10.1109/DICTA.2018.8615765","DOIUrl":null,"url":null,"abstract":"3D reconstruction of neuronal morphology is crucial to solving neuron-related problems in neuroscience as it is a key technique for investigating the connectivity and functionality of the neuron system. Many methods have been proposed to improve the accuracy of digital neuron reconstruction. However, the large amount of computer memory and computation time they require to process the large-scale images have posed a new challenge for us. To solve this problem, we introduce a novel Memory (and Time) Efficient Image Tracing (MEIT) framework. Evaluated on the Gold dataset, our proposed method achieves better or competitive performance compared to state-of-the-art neuron tracing methods in most cases while requiring less memory and time.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Digital Image Computing: Techniques and Applications (DICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA.2018.8615765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

3D reconstruction of neuronal morphology is crucial to solving neuron-related problems in neuroscience as it is a key technique for investigating the connectivity and functionality of the neuron system. Many methods have been proposed to improve the accuracy of digital neuron reconstruction. However, the large amount of computer memory and computation time they require to process the large-scale images have posed a new challenge for us. To solve this problem, we introduce a novel Memory (and Time) Efficient Image Tracing (MEIT) framework. Evaluated on the Gold dataset, our proposed method achieves better or competitive performance compared to state-of-the-art neuron tracing methods in most cases while requiring less memory and time.
大规模图像中记忆和时间效率高的三维神经元形态跟踪
神经元形态的三维重建对于解决神经科学中神经元相关问题至关重要,因为它是研究神经元系统连接和功能的关键技术。为了提高数字神经元重建的准确性,人们提出了许多方法。然而,处理大规模图像需要大量的计算机内存和计算时间,这对我们提出了新的挑战。为了解决这个问题,我们引入了一种新的记忆(和时间)高效图像跟踪(MEIT)框架。在Gold数据集上进行了评估,在大多数情况下,我们提出的方法与最先进的神经元跟踪方法相比,在需要更少的内存和时间的情况下实现了更好或更具竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信