Engineering a data processing pipeline for an ultra-lightweight lensless fluorescence imaging device with neuronal-cluster resolution

Zihao Yu, Mark Christian S.G. Guinto, Brian Godwin S. Lim, Renzo Roel P. Tan, Junichiro Yoshimoto, Kazushi Ikeda, Yasumi Ohta, Jun Ohta

Research output: Contribution to journalArticlepeer-review

Abstract

In working toward the goal of uncovering the inner workings of the brain, various imaging techniques have been the subject of research. Among the prominent technologies are devices that are based on the ability of transgenic animals to signal neuronal activity through fluorescent indicators. This paper investigates the utility of an original ultra-lightweight needle-type device in fluorescence neuroimaging. A generalizable data processing pipeline is proposed to compensate for the reduced image resolution of the lensless device. In particular, a modular solution centered on baseline-induced noise reduction and principal component analysis is designed as a stand-in for physical lenses in the aggregation and quasi-reconstruction of neuronal activity. Data-driven evidence backing the identification of regions of interest is then demonstrated, establishing the relative superiority of the method over neuroscience conventions within comparable contexts.

Original languageEnglish
Pages (from-to)483-495
Number of pages13
JournalArtificial Life and Robotics
Volume28
Issue number3
DOIs
Publication statusPublished - 08-2023

All Science Journal Classification (ASJC) codes

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence

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