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High-content screening (HCS) has catalyzed drug development through enabling fast, large-scale, and reproducible testing of changes in cellular invitro models in response to different types of perturbations. One HCS approach, known as Cell Painting (CP), can conduct the morphological profiling of images containing cells perturbed with different treatments to quantitatively assess complex biological changes. Profiling stages of macrophage polarization, in particular, enables new drug discovery with disease-relevant conditions. To analyze cell images at single cell level, deep learning algorithms - in addition to classical image segmentation methods - may also be used to conduct single cell cropping for accurate and fast detection of individual cells. While the classical Watershed Segmentation and Gaussian mixture model (GMM) was first implemented for robust single-cell detection in the CP workflow, its performance is sometimes compromised when cells are clumped. A deep learning-based cell segmentation method called Cellpose was introduced and proposed as an alternative means for cell localization, however, coming at the cost of compromised runtime for HCS. In this study, we demonstrate the use of YOLOv5, a fast deep learning object detection algorithm, to yield comparable cell detection performance to the other two methods, while bringing improvements in high cell density regions and a faster runtime. This study demonstrates the use of the YOLOv5 model for performing ~2x faster cell detection with comparable IoU scores on HCS macrophage nuclei images, demonstrating its value in extracting coordinates for single-cell cropping needed in deep learning-based phenotypic profiling in HCS. We compare the accuracy and speed of the model developed using YOLOv5 with those of the current Watershed/GMM method and Cellpose method in macrophage cell detection in the context of investigating drug activity.
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