Next Article in Journal
Liver Growth Factor Induces Glia-Associated Neuroprotection in an In Vitro Model of Parkinson´s Disease
Previous Article in Journal
Erratum: Pagonabarraga, J.; et al. A Spanish Consensus on the Use of Safinamide for Parkinson’s Disease in Clinical Practice. Brain Sci. 2020, 10, 176
Previous Article in Special Issue
Structural Characteristic of the Arcuate Fasciculus in Patients with Fluent Aphasia Following Intracranial Hemorrhage: A Diffusion Tensor Tractography Study
Open AccessArticle

Massive Data Management and Sharing Module for Connectome Reconstruction

1
School of Automation, Harbin University of Science and Technology, Harbin 150080, China
2
Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
The National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
5
The School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Brain Sci. 2020, 10(5), 314; https://doi.org/10.3390/brainsci10050314
Received: 30 April 2020 / Revised: 19 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability. View Full-Text
Keywords: connectome; massive data management; distributed storage and retrieval; electron microscope image; segmentation result; image cache connectome; massive data management; distributed storage and retrieval; electron microscope image; segmentation result; image cache
Show Figures

Figure 1

MDPI and ACS Style

Yuan, J.; Zhang, J.; Shen, L.; Zhang, D.; Yu, W.; Han, H. Massive Data Management and Sharing Module for Connectome Reconstruction. Brain Sci. 2020, 10, 314.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop