The method of live-cell imaging, taken as long-term time-lapses, is important when studying dynamic biological processes. Fluorescent labeling techniques allow the study of certain structures and cell components, in particular to trace dynamic processes over time, such as changes in intensity and spatial distribution of fluorescent signals. We also give an insight into quality assurance methods, which help to ensure good scientific practice when modernizing BIA workflows and refactoring code.Ĭell membranes create functional compartments and maintain diverse content and activities. Accordingly, we show how to measure workflow performance. In terms of image processing, refactoring means restructuring an existing macro without changing measurement results, but rather improving processing speed. These commands are then assembled to refactor the pre-existing workflow. We then introduce ways to discover CLIJ commands as counterparts of classic ImageJ methods. To demonstrate the procedure, we translate a formerly published BIA workflow for examining signal intensity changes at the nuclear envelope, caused by cytoplasmic redistribution of a fluorescent protein (Miura, 2020). Our suggested approach neither requires a profound expertise in high performance computing, nor to learn a new programming language such as OpenCL. We present a guide for transforming state-of-the-art image processing workflows into GPU-accelerated workflows using the ImageJ Macro language. , 2020), enables biologists and bioimage analysts to speed up time-consuming analysis tasks by adding support for the Open Computing Language (OpenCL) for programming GPUs (Khronos-Group, 2020) in ImageJ. As an alternative to established acceleration techniques, such as ImageJ’s batch mode, we explore how GPUs can be exploited to accelerate classic image processing. Even though general machine learning and convolutional neural networks are not new approaches to image processing, their importance for life science is increasing.Īs their application is now at hand due to the rise of advanced computing hardware, namely graphics processing units (GPUs), a natural question is if GPUs can also be exploited for classic image processing in ImageJ (Schneider et al. Nowadays, image data scientists join forces with artificial intelligence researchers, incorporating more and more machine learning algorithms into BIA workflows. Modern life science increasingly relies on microscopic imaging followed by quantitative bioimage analysis (BIA).
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