A multiscale parallel computing architecture for automated segmentation of the brain connectome.
A multiscale parallel computing architecture for automated segmentation of the brain connectome.
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DOI：
10.1109/tbme.2011.2168396
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发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
Reid RC
中科院分区：
文献类型：
--
作者：
Jaume S;Knobe K;Newton RR;Schlimbach F;Blower M;Reid RC
神经生物学的几个研究小组已经开始通过对老鼠大脑进行大规模成像和手动追踪神经元之间的连接来破译大脑回路。绘制大脑回路图，也被称为连接组，可能会对理解阿尔茨海默病等神经退行性疾病产生巨大影响。尽管老鼠的大脑比人脑小得多，但它已经展示了10亿个连接，手工追踪老鼠大脑的连接组只能部分实现。本文提出采用自动图像分割和为领域专家设计的并行计算方法来扩大跟踪规模。我们解释了并行方法背后的设计决策，并展示了我们在没有任何人工干预的情况下获得的血管和细胞核分割的结果。
Several groups in neurobiology have embarked into deciphering the brain circuitry using large-scale imaging of a mouse brain and manual tracing of the connections between neurons. Creating a graph of the brain circuitry, also called a connectome, could have a huge impact on the understanding of neurodegenerative diseases such as Alzheimer’s disease. Although considerably smaller than a human brain, a mouse brain already exhibits one billion connections and manually tracing the connectome of a mouse brain can only be achieved partially. This paper proposes to scale up the tracing by using automated image segmentation and a parallel computing approach designed for domain experts. We explain the design decisions behind our parallel approach and we present our results for the segmentation of the vasculature and the cell nuclei, which have been obtained without any manual intervention.