Zihao Yu, Mark Christian S. G. Guinto, Brian Godwin S. Lim, Renzo Roel P. Tan, Junichiro Yoshimoto, Kazushi Ikeda, Yasumi Ohta, Jun Ohta
{"title":"Engineering a data processing pipeline for an ultra-lightweight lensless fluorescence imaging device with neuronal-cluster resolution","authors":"Zihao Yu, Mark Christian S. G. Guinto, Brian Godwin S. Lim, Renzo Roel P. Tan, Junichiro Yoshimoto, Kazushi Ikeda, Yasumi Ohta, Jun Ohta","doi":"10.1007/s10015-023-00875-x","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"28 3","pages":"483 - 495"},"PeriodicalIF":0.8000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-023-00875-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
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.