Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling.

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.

Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography.

OBJECTIVE: In this paper we introduce a methodology for hard and soft tissue quantification at proximal, intermediate and distal tibia sites using peripheral quantitative computed tomography scans. Quantification of bone properties is crucial for estimating bone structure resistance to mechanical stress and adaptations to loading. Soft tissue variables can be computed to investigate muscle volume and density, muscle-bone relationship, and fat infiltration. APPROACH: We employed implicit active contour models and clustering techniques for automated segmentation and identification of bone, muscle and fat at [Formula: see text], [Formula: see text], and [Formula: see text] tibia length. Next, we calculated densitometric, area and shape characteristics for each tissue type. We implemented our approach as a multi-platform tool denoted by TIDAQ (tissue identification and quantification) to be used by clinical researchers. MAIN RESULTS: We validated the proposed method against reference quantification measurements and tissue delineations obtained by semi-automated workflows. The average Deming regression slope between the tested and reference method was 1.126 for cross-sectional areas and 1.078 for mineral densities, indicating very good agreement. Our method produced high average coefficient of variation (R 2) estimates: 0.935 for cross-sectional areas and 0.888 for mineral densities over all tibia sites. In addition, our tissue segmentation approach achieved an average Dice coefficient of 0.91 over soft and hard tissues, indicating very good delineation accuracy. SIGNIFICANCE: Our methodology should allow for high throughput, accurate and reproducible automatic quantification of muscle and bone characteristics of the lower leg. This information is critical to evaluate risk of future adverse outcomes and assess the effect of medications, hormones, and behavioral interventions aimed at improving bone and muscle strength.

Imaging and tracking HIV viruses in human cervical mucus.

We describe a systematic approach to image, track, and quantify the movements of HIV viruses embedded in human cervical mucus. The underlying motivation for this study is that, in HIV-infected adults, women account for more than half of all new cases and most of these women acquire the infection through heterosexual contact. The endocervix is believed to be a susceptible site for HIV entry. Cervical mucus, which coats the endocervix, should play a protective role against the viruses. Thus, we developed a methodology to apply time-resolved confocal microscopy to examine the motion of HIV viruses that were added to samples of untreated cervical mucus. From the images, we identified the viruses, tracked them over time, and calculated changes of the statistical mean-squared displacement (MSD) of each virus. Approximately half of tracked viruses appear constrained while the others show mobility with MSDs that are proportional to ??+?2?2, over time range ?, depicting a combination of anomalous diffusion (0

Joint level-set and spatio-temporal motion detection for cell segmentation.

BACKGROUND: Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. METHODS: In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. RESULTS: We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. CONCLUSIONS: Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.