Protocol for Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro

Abstract

Abstract Regular image analysis derived from confocal microscopic data of biological tissue samples with the Imaris software package involves a significant amount of repetition on the part of the researchers for each image. We developed a supervised automation protocol in our lab to execute our everyday cell and spot counting tasks (e.g., neuron, nucleus, macrophages) on images obtained from mouse cochlear samples. The proposed semi-automated protocol can be reproduced and customized by scientists working on basic and experimental neuro-immunology and neuroanatomy research and on other disciplines as well. Though it has some limitations and challenges, the protocol can be further enhanced with the integration of machine learning. We believe our developed protocol may enlighten the scientific community on the controlled automation of image analysis tasks, may translate to a substantial reduction of the manual labor of the researchers, and can increase laboratory efficiency and productivity.