The automated analysis of microscopy images is a challenge in the context of single-cell tracking and quantification. This work has as goals the study of the performance of deep learning for segmenting microscopy images and the improvement of the previously available pipeline for tracking single cells. Deep learning techniques, mainly convolutional neural networks, have been applied to cell segmentation problems and have shown high accuracy and fast performance. To perform the image segmentation, an analysis of hyperparameters was done in order to implement a convolutional neural network with U-Net architecture. Furthermore, different models were built in order to optimize the size of the network and the number of learnable parameters. The trained network is then used in the pipeline that localizes the traps in a microfluidic device, performs the image segmentation on trap images, and evaluates the fluorescence intensity and the area of single cells over time. The tracking of the cells during an experiment is performed by image processing algorithms, such as centroid estimation and watershed. Finally, with all improvements in the neural network to segment single cells and in the pipeline, quasi-real-time image analysis was enabled, where 6.20GB of data was processed in 4 minutes.
翻译:在单细胞追踪和量化方面,自动分析显微镜图像是一项挑战,对显微镜图像进行自动分析是单细胞追踪和量化方面的一项挑战。这项工作的目标是研究如何进行深入学习,以进行分解显微镜图像的深度学习,并改进以前可用于跟踪单细胞的管道。深层学习技术,主要是神经神经神经网络,已应用于细胞分解问题,并显示高精度和快速性能。为进行图像分层,对超光谱进行了分析,以便利用 U-Net 结构实施一个神经网络;此外,还建立了不同的模型,以优化网络的规模和可学习参数的数量。然后,在管道中使用了经过培训的网络,将微氟化装置中的陷阱本地化,对捕捉图像进行图像分解,并评价荧光强度和单个细胞的面积。在试验期间,通过图像处理算法对细胞进行跟踪,例如中子估计和流域。最后,随着神经网络对单细胞部分细胞和管道进行所有改进,使准实时图像分析得以进行。