The group has begun collecting time-lapse microscopy videos of mammalian cells. The data includes image sequences of hundreds of Balb/c fibroblast cells on polyacrylamide hydrogel substrates with varying mechanical properties and on tissue culture plastic substrates. The living cells were seeded on the substrates at a density of 1,000 cells/cm^2. The images of 1300 by 1030 pixels were acquired with a Princeton Instruments D1299421 camera mounted on a Zeiss Axiovert S100 microscope over the course of 10-24 hours.
The group first experimented with an interval between images of 15 minutes and then extended our capture rate to 30 seconds. It established ground-truth classification of cell shape for 800 images with about 2-10 cells per image. The class label indicate whether a cell is "spread," "non-spread," "polarized," etc. The group is in the process of developing a machine learning approach to classify cell shape in single images of cells.
The group has established ground-truth motion trajectories in image sequences of over 400 cells. Using these image sequences, the group will develop and test the performance of various tracking algorithms, including algorithms that evaluate cell shape. To solve the data association problem, the assignment of current measurements to cell tracks, the group has tested various cost functions with both an optimal and a fast, suboptimal assignment algorithm (see video below).
The group measured how cell populations respond to the physical stimuli presented in the environment, for example, the stiffness property of the substrate. The analysis of hundreds of spatio-temporal cell trajectories revealed significant differences in the behavioral response of fibroblast cells to changes in hydrogel conditions.
The group also proposed metrics to quantify cell migration properties, such as motility and directional persistence. The group is in the process of comparing actual measurements of cell movement with a theory, the "random walk model," which has been used widely in the literature to describe cell migration.Preliminary results were published a workshop paper
D. House, M. L. Walker, Z. Wu, J. Y. Wong, and M. Betke. "Tracking of cell populations to understand their spatio-temporal behavior in response to physical stimuli." In MMBIA 2009: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, Miami, FL, June 2009. 8 pp. pdf.and discussed in a conference poster
M. L. Walker, D. M. House, M. Betke, and J. Y. Wong. "Using automated cell tracking tools to quantify durokinesis and durotaxis in real time." Proceedings of the Biophysical Society 53rd Annual Meeting, Boston, MA, USA, February 2009. Abstract.We used machine learning techniques to classify cells based on their shape:
D. H. Theriault, M. Walker, J. Y. Wong, and M. Betke. "Cell Morphology Classification and Clutter Mitigation in Phase-Contrast Microscopy Images Using Machine Learning." Machine Vision and Applications, 23(4):659-673, 2012, paper on Springer, local pdf (restricted access).
Segmentation of Interacting Cells
Cell shape analysis plays an important role for studying biological processes, developing biomaterials, and diagnosing and fighting diseases. The goal of this work is to address a key bottleneck in computer vision systems that automatically perform such analysis with accurately maintaining shape recognition of individual cells as they undergo continuous deformation while interacting with other deforming cells over long durations. This method relies on the observation that the boundary of the merged object is made up of segments of the individual object boundaries from before they merged. We published this work at MICCAI:
Z. Wu, D. Gurari, J. Y. Wong, and M. Betke, "Hierarchical Partial Matching and Segmentation of Interacting Cells." Proceedings of the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, October 1-5, 2012. 8 pp., Poster. Pdf. Video.
Analysis of Segmentation Quality
Segmentation analysis is important for establishing that contours of objects found accurately represent the true contours of objects observed in images. The common approach for segmentation analysis involves selecting a measure and then computing a score indicating how similar the query segmentation is to the gold standard segmentation. Observing that the score depends on the gold standard segmentation, we proposed an analysis framework that introduces the consideration of how to establish the gold standard segmentation. We call this framework SAGE standing for Segmentation Annotation Collection, Gold Standard Generation, and Evaluation. We conducted three case studies addressing how to establish trusted gold standard segmentations for cell and artery images. We prepared a freely available implementation of SAGE and published our work at WACV:
D. Gurari, S. Kim, E. Yang, B. Isenberg, T. Pham, A. Purwada, P. Solski, M. Walker, J. Y. Wong, and M. Betke, "SAGE: An Approach and Implementation Empowering Quick and Reliable Quantitative Analysis of Segmentation Quality," Proceedings of the Workshop on Applications in Computer Vision (WACV), Clearwater, Florida, January 17-18, 2013. 7 pp. pdf. Best Paper Award. One of two awards selected among 161 submitted and 75 accepted papers.
Video of tracking cells in time-lapse microscopy images
Active Contour Segmentation
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