Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking

Automated Bright Field Segmentation of Cells and Vacuoles Using Image Processing Technique

Understanding the mechanisms and other variants of programmed cell death will help provide deeper insight into various disease processes. Although complex procedures are required to distinguish each type of cell death, the formation of vacuoles is one of the important features in some process of cell death under different conditions. Thus, monitoring and counting the number of vacuoles and the ratio of cells with vacuoles is a commonly used method to indicate and quantify the efficacy of the therapy. Several studies have shown that image processing can provide a quick, convenient and precise mean of performing cell detection. Hence, this study uses an image processing technique to detect and quantify vacuolated cells without the need for dyes. The system both counts the number of vacuolated cells and determines the ratio of cells with vacuoles. The performance of the proposed image processing system was evaluated using 38 images. It has been shown that a strong correlation exists between the automated counts and the manual counts. Furthermore, the absolute percentage errors between automated counts and manual counts for cell detection and vacuolated cell detection using data pooled from all images are 3.61 and 3.33%, respectively. A user-friendly graphical user interface (GUI) is also developed and freely available for download, providing researchers in biomedicine with a more convenient instrument for vacuolization analysis.

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%.