Time-lapse Microscopy Image Sequences Research Articles

Отслеживание плавания самодвижущихся сперматозоидов с помощью компьютерных методов анализа спермы для определения влияния вибрации на сперматозоиды

Human sperm motility is of great interest to researchers and biologists studying sperm function and to medical practitioners evaluating and treating male in-fertility. Computer-assisted semen analysis (CASA) instruments are used to trace the swimming paths of sperm automatically in time-lapse microscopy image sequences connect-the-dots between video frames, enabling rapid automatic quantification of sperm motility parameters for hundreds of cells at a time. The image processing and sperm tracking capabilities of today’s CASA instruments can be enhanced However. In this paper we highlighted the limitations of (CASA) instruments and suggested ways to mitigate them from the literature. We have also our algorithm ‒ program complex to determine linear velocity in Java. With all these efforts in further improving the computational tools for CASA, much more robust and versatile systems should be available in the foreseeable future.

Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy.

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images.

We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer an image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics. Adapting the well-known joint probabilistic data association filter (JPDAF), we automatically tracked hundreds of human sperm simultaneously and measured their dynamic swimming parameters over time. Unlike existing CASA instruments, our algorithm has the capability to track sperm swimming in close proximity to each other and during apparent cell-to-cell collisions. Collecting continuously parameters for each sperm tracked without sample dilution (currently impossible using standard CASA systems) provides an opportunity to compare such data with standard fertility rates. The use of our algorithm thus has the potential to free the clinician from having to rely on elaborate motility measurements obtained manually by technicians, speed up semen processing, and provide medical practitioners and researchers with more useful data than are currently available.

Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning

We propose using machine learning techniques to analyze the shape of living cells in phase-contrast microscopy images. Large scale studies of cell shape are needed to understand the response of cells to their environment. Manual analysis of thousands of microscopy images, however, is time-consuming and error-prone and necessitates automated tools. We show how a combination of shape-based and appearance-based features of fibroblast cells can be used to classify their morphological state, using the Adaboost algorithm. The classification accuracy of our method approaches the agreement between two expert observers. We also address the important issue of clutter mitigation by developing a machine learning approach to distinguish between clutter and cells in time-lapse microscopy image sequences.

ProgeniDB: A Novel Cell Lineage Database for Generation Associated Phenotypic Behavior in Cell-based Assays

ProgeniDB is a web-accessible cell lineage database, which comprises data derived from time-lapse microscopy image sequences, using our novel encoding software tool called ProgeniTRAK. This database provides information on qualitative and quantitative changes in cellular dynamics in response to anticancer drugs variation, which is critical for pharmacodynamic modeling and validation. The lineage data for each progenitor cell centers around the construction of a bifurcation map, locating in both space and time critical cellular events such as cell division and cell death. This database provides the opportunity to select a set of lineages based on different experimental constraints and observe the associated phenotypic behavior of progenitor cells. The output generated from this database depicts the cellular dynamic of selected population which can be downloaded to explore and exploit in relation to hypothesis-driven drug discovery. A case study is presented where we interrogate drug induced perturbations on ensuing cell lineages. The database is available at http://biodiversity.cs.cf.ac.uk/cymart/progenidb.html