From cells to pixels: A decision tree for designing bioimage analysis pipelines

Abstract Bioimaging has transformed our understanding of biological processes, yet extracting meaningful information from complex datasets remains a challenge, particularly for biologists without computational expertise. This paper proposes a simple general approach, to help identify which image analysis methods could be relevant for a given image dataset. We first categorise structures commonly observed in bioimage data into different types related to image analysis domains. Based on these types, we provide a list of methods adapted to the quantification of images from each category. Our approach includes illustrative examples and a visual flowchart, to help researchers define analysis objectives clearly. By understanding the diversity of bioimage structures and linking them with appropriate analysis approaches, the framework empowers researchers to navigate bioimage datasets more efficiently. It also aims to foster a common language between researchers and analysts, thereby enhancing mutual understanding and facilitating effective communication.