Cellular tracking in time-lapse phase contrast images

The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes are difficult issues that have to be accommodated by automatic tracking techniques. In this paper, we detail the development of a fully automated multi-target tracking system that is able to deal with Brownian motion and cellular division. During the tracking process our approach includes the neighbourhood relationship and motion history to enforce the cellular tracking continuity in the spatial and temporal domain. The experimental results reported in this paper indicate that our method is able to accurately track cellular structures in time-lapse data

A novel framework for tracking in-vitro cells in time-lapse phase contrast data

With the proliferation of modern microscopy imaging technologies the amount of data that has to be analysed by biologists is constantly increasing and as a result the development of automatic approaches that are able to track cellular structures in timelapse images has become an important field of research. The aim of this paper is to detail the development of a novel tracking framework that is designed to extract the cell motility indicators in phase-contrast image sequences. To address issues that are caused by nonstructured (random) motion and cellular agglomeration, cell tracking is formulated as a sequential process where the inter-frame cell association is achieved by assessing the variation in the local structures contained in consecutive frames of the image sequence. We have evaluated the proposed algorithm on dense phase contrast cellular data and the reported results indicate that the developed algorithm is able to accurately track MadinDarby Canine Kidney (MDCK) Epithelial Cells in image data that is characterised by low contrast and high level of noise

A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images

The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by non-structured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the inter-frame cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy

Automatic cellular segmentation in time-lapse phase contrast images

The process of cellular detection and tracking is a key task in the analysis of cellular motility and proliferation. The current clinical procedure involves a time consuming procedure that requires the manual annotation of cells in sequences of time-lapse phase contrast microscopy images. With the development of modern imaging modalities, the amount of data to be interpreted by biologists is constantly increasing, thus the development of automatic techniques that are able to detect cellular structures in large image sequences is more necessary than ever before. Robust cellular detection represents the first step in the development of cellular tracking algorithms and one of the objectives of our work was focused on the development of an automatic technique that is able to segment the cells in various sequences of cellular data. The proposed segmentation framework adaptively determines the criteria to separate the cells and the background and additional morphological operations are applied to detect the initial structures that define the cells in each image of the sequence. The initial segmentation results are refined by applying motion consistency constraints to detect the cells that are missed by the initial segmentation process due to factors such as image noise and low contrast. In our experiments we have applied the proposed segmentation framework to NE4C, MDCK and HUVEC cellular data. A number of experimental results are illustrated in Figure 1

Cell segmentation in time-lapse phase contrast data

The quantitative analysis of cellular migration has found many clinical applications as it can be used in the study of a large spectrum of biological processes such as tumor development and wound healing. These studies are commonly conducted on datasets that consists of a large number of timelapse images, a fact that rendered the application of human assisted procedures as unfeasible, especially when applied to large datasets. In the development of automatic tracking strategies the problem of robust cell segmentation plays a central role as the segmentation errors have adverse effects on the performance of the overall tracking process. While the phase contrast image data is often characterized by low contrast, changes in the morphology of the cells over time and cell agglomeration, the cell segmentation process is far from a trivial task. In this paper we present a new cell segmentation approach that maximizes the information related to the local contrast between the cells and the background in each image of the dataset. The proposed method has been evaluated on MDCK and HUVEC cellular datasets and experimental results are reported.</p