The deformation process orchestrated by the cell machinery is particularly complex and characterized by shape changes at multiple geometrical scales to drive cell deformation and movement. Precise cell shape description tools have become necessary to detect shape changes in a robust manner to allow subsequent morpho-dynamic profiling. Here we investigate the use of spherical wavelets to perform a multi-scale analysis of the cell shape, in order to automatically detect and track protrusions at the cell surface. Examples on real biological data highlight encouraging results and open the way to a powerful description of the cell deformation process.

Abstract Motivation: In developmental biology, quantitative tools to extract features from fluorescence microscopy images are becoming essential to characterize organ morphogenesis at the cellular level. However, automated image analysis in this context is a challenging task, owing to perturbations induced by the acquisition process, especially in organisms where the tissue is dense and opaque. Results: We propose an automated framework for the segmentation of 3D microscopy images of highly cluttered environments such as developing tissues. The approach is based on a partial differential equation framework that jointly takes advantage of the nuclear and cellular membrane information to enable accurate extraction of nuclei and cells in dense tissues. This framework has been used to study the developing mouse heart, allowing the extraction of quantitative information such as the cell cycle duration; the method also provides qualitative information on cell division and cell polarity through the creation of 3D orientation maps that provide novel insight into tissue organization during organogenesis. Availability: The proposed framework is free, open-source and available on the Icy platform (http://www.icy.bioimageanalysis.org/). Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

We aim at analysing fluorescence microscopy sequences of moving cells, so as to estimate the velocities that define the movement, the forces that drive this movement, and the pressure that characterises the material state. We use an optical flow method constrained by Stokes flow mechanics equations that define the dynamics of a fluid that is incompressible, homogeneous and viscous. We present a finite element formulation that leads to a quadratic programming problem. We apply the method to real biological data of amoebas observed by fluorescence microscopy.

Image filtering methods are crucial in a large number of applications where the relevant information is either noisy or partially missing. More particularly in 3D microscopy, cell membranes appear as structures of co-dimension 2, and suffer from discontinuities in intensity and orientation. Anisotropic PDE-based approaches offer an elegant solution to these problems, by integrating spatial and orientation information in a flexible and robust manner. In this work we propose a new tensor-based anisotropic model specifically adapted to 2D planar structures able to perform noise reduction and contrast enhancement simultaneously. Evaluation on synthetic and real data as well as comparison with previous works show that the model is well adapted for cell membrane structures.

Variational deformable models have proven over the past decades a high efficiency for segmentation and tracking in 2-D sequences. Yet, their application to 3-D time-lapse images has been hampered by discretization issues, heavy computational loads and lack of proper user visualization and interaction, limiting their use for routine analysis of large data-sets. We propose here to address these limitations by reformulating the problem entirely in the discrete domain using 3-D active meshes, which express a surface as a discrete triangular mesh, and minimize the energy functional accordingly. By performing computations in the discrete domain, computational costs are drastically reduced, whilst the mesh formalism allows to benefit from real-time 3-D rendering and other GPU-based optimizations. Performance evaluations on both simulated and real biological data sets show that this novel framework outperforms current state-of-the-art methods, constituting a light and fast alternative to traditional variational models for segmentation and tracking applications.

This paper presents a novel approach to track multiple articulated objects in a video sequence. The key idea is to define a model of the object using a set of geometrical primitives linked by physical constraints, and exploit physics engines to solve these constraints while the model adapts to the object under the influence of local mean-shift processes. This novel approach to object tracking has numerous advantages: the model provides rich geometric information about the object at the articulation level; multiple touching objects are implicitly distinguished using collision detection strategies; physics engines are able to efficicently manage both image-based and model-based constraints simultaneously for a neglectable computational cost, suggesting their potential interest for many more image processing applications.

Cellular endocytosis is a mechanism of great interest in biology, for it regulates the communication between the cell and the external medium. With recent advances in fluorescence microscopy, endocytosis has become a popular candidate for image-based high content screening campains. In this context, we have developed an automated framework comprising robust cell segmentation using coupled shape-constrained active contours and efficient endosome extraction using an isotropic undecimated wavelet transform. The resulting method has few parameters and is able to analyze tens of cells per image in the order of seconds. Validation is performed by experimentally confirming previously published results obtained through manual analysis.

Cell migrations and deformations play essential roles in biological processes, such as parasite invasion, immune response, embryonic development, and cancer. We describe a fully automatic segmentation and tracking method designed to enable quantitative analyses of cellular shape and motion from dynamic three-dimensional microscopy data. The method uses multiple active surfaces with or without edges, coupled by a penalty for overlaps, and a volume conservation constraint that improves outlining of cell/cell boundaries. Its main advantages are robustness to low signal-to-noise ratios and the ability to handle multiple cells that may touch, divide, enter, or leave the observation volume. We give quantitative validation results based on synthetic images and show two examples of applications to real biological data.