A visual targeting system for the microinjection of unstained adherent cells.

Automatic localization and targeting are critical steps in automating the process of microinjecting adherent cells. This process is currently performed manually by highly trained operators and is characterized as a laborious task with low success rate. Therefore, automation is desired to increase the efficiency and consistency of the operations. This research offers a contribution to this procedure through the development of a vision system for a robotic microinjection setup. Its goals are to automatically locate adherent cells in a culture dish and target them for a microinjection. Here the major concern was the achievement of an error-free targeting system to guarantee high consistency in microinjection experiments. To accomplish this, a novel visual targeting algorithm integrating different image processing techniques was proposed. This framework employed defocusing microscopy to highlight cell features and improve cell segmentation and targeting reliability. Three main image processing techniques, operating at three different focus levels in a bright field (BF) microscope, were used: an anisotropic contour completion (ACC) method, a local intensity variation background-foreground classifier, and a grayscale threshold-based segmentation. The proposed framework combined information gathered by each of these methods using a validation map and this was shown to provide reliable cell targeting results. Experiments conducted with sets of real images from two different cell lines (CHO-K1 and HEK), which contained a total of more than 650 cells, yielded flawless targeting results along with a cell detection ratio greater than 50%.