Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations. Experiments where live cell samples are imaged over prolonged periods of time have the potential to help us understand how cells behave and respond to changes in the local environment. Modern imaging experiments, with ever increasing scale, rule out manual/visual image processing and analysis. A central problem in many automated analysis approaches, especially those following individual cells over time, is image segmentation. Bright-field imaging is currently the least invasive microscopy imaging approach allowing long time-lapse studies, it is available on all microscopes, and is the cheapest microscopy method to implement. However, automated segmentation of unstained cells imaged by bright-field microscopy is typically very challenging, and many of the common approaches, as reviewed by Meijering 1 , fail. Recently, it has been shown that methods based on Deep Convolutional Neural Networks (DCNNs) 2-4 solve segmentation problems very difficult to solve using traditional image processing methods. Traditional image segmentation approaches often require experiment-specific parameter tuning, while DCNNs instead require training data. Large amounts of high-quality annotated samples, or ground truth is typically required for DCNN training. The ground truth represents the extent to which an object is present in an image in reality. In an image the extent of a particular object is specified by the boundary of the object, usually created by a domain expert. Visual/manual annotation is tedious, cumbersome and expensive. The annotated samples for one dataset may not be useful for another dataset, and new ground truth generation may be needed for every new dataset, limiting the usability of DCNNs. Manually created ground truths have been used to segment both bacterial and mammalian cells 5 . Automated training has previously been suggested for classification of cells from imaging flow cytometry data where each image contains a single cell in liquid solution 6 . We propose an automated approach for creation of high-quality experiment-specific ground truth for segmentation of bright-field images of cultured cells based on end-point fluorescent staining. We capture bright-field images during the entire period of the experiment (Fig. 1a) . At the end of the experiment, we stain the cells with fluorescent markers for nuclei and cytoplasmic regions. After staining, we capture fluorescent images along with bright-field data (Fig. 1b, Supplementary Fig. 1 ). We apply standard automated methods available in CellProfiler 7 to segment the cells based on the information from the fluorescent channels. Finally, we use these automatically created segmentation results as labels along with the corresponding bright-field image data to train a DCNN (Fig. 1b) . To handle slight variations in focal depth, inspired by Liimatainen et al. , we collected bright-field data in a z-stack at three consecutive focus levels both for training and for the time-lapse experiment. The trained DCNN then segments all images in the full time-lapse sequence (Fig. 1c, Supplementary Fig. 3) . We provide open-source code for this fully automated image analysis approach enabling biologists to use DCNN-based segmentation in their routine live cell experiments (Supplementary Software). We also show that the general idea is applicable for other microscopy experiments where object-specific fluorescent stains can be used for definition of training regions.
• In REF ) the problem of cultured cell segmentation was considered.
Automated Training of Deep Convolutional Neural Networks for Cell Segmentation
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Scientific Reports
Computer Science Medicine