Keywords: 3D biological imaging, multiple cell tracking, active contours
Summary: Elucidating the mechanisms of cell deformation and motility is a topic of major interest in cell biology, for they are strongly involved in cell development, immune responses, cancer and infectious diseases. Our aim to develop robust and fully automated image analysis tools, able to extract quantitative measures of cell dynamics from multi-dimensional (2D/3D), multi-modal (brightfield/phase-contrast/fluorescence) time-lapse microscopy data. It is nowadays a growing consensus that the combination of large-scale cell imaging and sophisticated computational analysis tools is necessary to provide new insights on numerous biological phenomena. Yet, many studies related to cell shape and motility rely still today on manual (though generally computer-assisted) analysis in 2D sequences. This process is cumbersome and prone to user bias and reproducibility issues. Migration to full 3D time-lapse experiments is a necessary step to reliably quantify cell shape information, but induces new challenges in terms of image acquisition and quantification, yielding a need for more robust and fully automated analysis techniques.
To this end, we have been focusing on the past years upon “active contours” techniques. The principle is to deform an initial contour placed on the image until it fits the boundary of the target cell. The deformation can be mathematically expressed as the minimization of an energy functional, which comprises several terms related either to the image data (driving the contour toward the cell boundary) or to geometrical properties of the contour (regularizing the deformation to avoid local energy minima). This energy is an essential ingredient of the method, and several of our contributions consist in adapting this energy and its implementation to meet the particular constraints of biological imaging, e.g., low signal-to-noise ratio, multiple cell-cell contacts over time, inhomogeneous cell staining, photo-bleaching, etc.. Active contours also offer a flexible formalism for efficient shape representation and quantification, allowing to compute robust statistics over large-scale cell populations.
The algorithms developed for this project are currently used through collaborations for numerous applications including multi-cell segmentation, tracking, interaction and particle localization.