Large scale neuron image storage solution using HDF5 file format – UROP Spring Symposium 2021

Large scale neuron image storage solution using HDF5 file format

Haochen Zhang


Pronouns: he him his

Research Mentor(s): Dawen Cai, Assistant Professor
Research Mentor School/College/Department: Cell and Developmental Biology; Biophysics; Neuroscience Graduate Program, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 18
Presenter: 6

Event Link


In Neuroscience and Neuro-biomedical research, it is crucial for researchers to understand the connections between different neurons to fully realize the mechanism for the brain’s functionality. We have proposed a light imaging-based approach to resolve neural circuits, that is first gathering Brainbow images from the microscope by scanning brain sample sections, and then neuronal structures, including synaptic connections can be reconstructed from these images. It is crucial to let researchers specifying requests that retrieving the spatial range and spectral channels of a subvolume of the images as the Brainbow image files generated from modern high-throughput microscopes are rather large. This causes bottlenecks not only for the software to deliver its functionality due to lack of memory but also for file transferring via the internet, which all brought the necessity of designing a new data storage system to efficiently store and process such files. Our project introduces a customized back-end infrastructure by storing compressed Brainbow images using the HDF5 file format that enables easy access and modification of subvolume images. We explored the influence of file segmentation size on the read and write performance of the Brainbow images, and the data compression and storing pipeline. Finally, we discuss the possibility of designing a distributed system for large scientific data hosting.

Authors: HAOCHEN ZHANG, Logan Walker, Dawen Cai
Research Method: Computer Programming

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