An Efficient Technique Based on Deep Learning for Automatic Focusing in Microscopic System

During microscopic examination process, experts firstly determine the most appropriate focusing point by precisely moving the microscope stage in the Z axis. This manual focusing process may cause variability in results depending on hand-eye coordination and individual experience. Therefore, the development of automatic focusing offers superior quality and consistent imaging. Many researchers have studied automatic focusing systems and proposed many focus functions to extract focus information from multi-focus images. Nevertheless, these focusing systems still consist of several significant limitations such as requiring a supplementary material, longer running time, producing different performance depending on the sample and magnification objective. In this study, an efficient technique based on deep learning is proposed to automatically determine the focused image during microscopic examination processes to minimize these limitations in the literature. This technique takes a sequence of images with the same field of view and different focuses as input and gives the image with maximum focusing as output. The proposed technique is compared with other automatic focusing techniques on the literature. In this study, novel multi-focus image sequences obtained from liver, intestine, heart, kidney, and lung samples is prepared to evaluate the performance of automatic focusing techniques. These sequences are obtained by scanning with 10× and 40× magnification objectives. Running time, accuracy, number of local maximum points, range, and noise levels are used as evaluation criteria in this study. According to evaluation criteria results obtained in this study, it has been proven that the proposed technique provides better performance than other automatic focusing techniques.