In recent years, there has been an explosion of fluorescence microscopy studies of live cells in the literature. The analysis of the images obtained in these studies often requires labor-intensive manual annotation to extract meaningful information. In this study, we explore the utility of a neural network approach to recognize, classify, and select plasma membranes in high-resolution images, thus greatly speeding up data analysis and reducing the need for personnel training for highly repetitive tasks. Two different strategies are tested: 1) a semantic segmentation strategy, and 2) a sequential application of an object detector followed by a semantic segmentation network. Multiple network architectures are evaluated for each strategy, and the best performing solutions are combined and implemented in the Recognition Of Cellular Membranes software. We show that images annotated manually and with the Recognition Of Cellular Membranes software yield identical results by comparing Förster resonance energy transfer binding curves for the membrane protein fibroblast growth factor receptor 3. The approach that we describe in this work can be applied to other image selection tasks in cell biology.
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