Arnaud Gelas

Specified Neural Progenitors Sort to Form Sharp Domains after Noisy Shh Signaling

Sharply delineated domains of cell types arise in developing tissues under instruction of inductive signal (morphogen) gradients, which specify distinct cell fates at different signal levels. The translation of a morphogen gradient into discrete spatial domains relies on precise signal responses at stable cell positions. However, cells in developing tissues undergoing morphogenesis and proliferation often experience complex movements, which may affect their morphogen exposure, specification, and positioning. How is a clear pattern achieved with cells moving around? Using in toto imaging of the zebrafish neural tube, we analyzed specification patterns and movement trajectories of neural progenitors. We found that specified progenitors of different fates are spatially mixed following heterogeneous Sonic Hedgehog signaling responses. Cell sorting then rearranges them into sharply bordered domains. Ectopically induced motor neuron progenitors also robustly sort to correct locations. Our results reveal that cell sorting acts to correct imprecision of spatial patterning by noisy inductive signals.

Interplay of Cell Shape and Division Orientation Promotes Robust Morphogenesis of Developing Epithelia

Epithelial cells acquire functionally important shapes (e.g., squamous, cuboidal, columnar) during development. Here, we combine theory, quantitative imaging, and perturbations to analyze how tissue geometry, cell divisions, and mechanics interact to shape the presumptive enveloping layer (pre-EVL) on the zebrafish embryonic surface. We find that, under geometrical constraints, pre-EVL flattening is regulated by surface cell number changes following differentially oriented cell divisions. The division pattern is, in turn, determined by the cell shape distribution, which forms under geometrical constraints by cell-cell mechanical coupling. An integrated mathematical model of this shape-division feedback loop recapitulates empirical observations. Surprisingly, the model predicts that cell shape is robust to changes of tissue surface area, cell volume, and cell number, which we confirm in vivo. Further simulations and perturbations suggest the parameter linking cell shape and division orientation contributes to epithelial diversity. Together, our work identifies an evolvable design logic that enables robust cell-level regulation of tissue-level development.

Detection of Spatially Correlated Objects in 3D Images Using Appearance Models and Coupled Active Contours

We consider the problem of segmenting 3D images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. Our approach involves an initial modeling phase followed by a data-fitting segmentation phase. In the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates. The nucleus is modeled for its shape as well as image intensity distribution inspired from the physics of its image formation. In the second phase, we solve the segmentation problem using a variational level-set strategy with coupled active contours to minimize a novel energy functional. We demonstrate the utility of our approach on multispectral fluorescence microscopy images.

Anisotropic plate diffusion filtering for detection of cell membranes in 3D microscopy images

We propose an anisotropic diffusion method to denoise and aid the reconstruction of planar objects in three-dimensional images. The contribution of this paper is the development of a planarity function characterizing plate-like structures using an image Hessians eigensystem. We then construct a diffusion tensor for anisotropically smoothing plates and satisfying necessary scale-space properties. Our method finds applications in improving the fidelity of highly noisy cell membrane images from confocal microscopy. In dense cellular regions, cell membranes assume linear shapes (planar) between neighbors. The imaging process makes cell membranes appear as diffuse structures owing to the non-uniform fluorescent marker distribution, point-spread function of the optics, and anisotropic voxel resolution which make automatic cell segmentation difficult. We apply diffusion filtering to identify and enhance membranes. We demonstrate the use of our methods on 3D cell membrane images of a zebrafish embryo acquired using fluorescent microscopy and quantify the improvement in image quality.

Technical Section: Variational implicit surface meshing

In this paper, we propose a new algorithm to mesh implicit surfaces which produces meshes both with a good triangle aspect ratio as well as a good approximation quality. The number of vertices of the output mesh is defined by the end-user. For this goal, we perform a two-stage processing: an initialization step followed by an iterative optimization step. The initialization step consists in capturing the surface topology and allocating the vertex budget. The optimization algorithm is based on a variational vertices relaxation and triangulation update. In addition a gradation parameter can be defined to adapt the mesh sampling to the curvature of the implicit surface. We demonstrate the efficiency of the approach on synthetic models as well as real-world acquired data, and provide comparisons with previous approaches.

Variational level-set with gaussian shape model for cell segmentation

In analysis of microscopy based images, a major challenge lies in splitting apart cells that appear to overlap because they are too densely packed. This task is complicated by the physics of the image acquisition that causes large variations in pixel intensities. Each image typically contains thousands of cells with each cell having a different orientation, size and intensity histogram. In this paper, a spatial intensity model of a nucleus is incorporated into [1] to aid cell segmentation from microscopy datasets. An energy functional is defined and with it the spatial intensity distribution of a nuclei is modeled as a Gaussian distribution with constant intensity background. Experimental results on a variety of microscopic data validate its effectiveness.

Streaming level set algorithm for 3D segmentation of confocal microscopy images

We present a high performance variant of the popular geodesic active contours which are used for splitting cell clusters in microscopy images. Previously, we implemented a linear pipelined version that incorporates as many cues as possible into developing a suitable level-set speed function so that an evolving contour exactly segments a cell/nuclei blob. We use image gradients, distance maps, multiple channel information and a shape model to drive the evolution. We also developed a dedicated seeding strategy that uses the spatial coherency of the data to generate an over complete set of seeds along with a quality metric which is further used to sort out which seed should be used for a given cell. However, the computational performance of any level-set methodology is quite poor when applied to thousands of 3D data-sets each containing thousands of cells. Those data-sets are common in confocal microscopy. In this work, we explore methods to stream the algorithm in shared memory, multi-core environments. By partitioning the input and output using spatial data structures we insure the spatial coherency needed by our seeding algorithm as well as improve drastically the speed without memory overhead. Our results show speed-ups up to a factor of six.

Tissue level segmentation and tracking of biological structures in microscopic images based on density maps

During embryogenesis, cells coordinate to form geometric arrangements. These arrangements are initially noticed as stereotypic clumps of cells that further divide to form a rigorous structure with a high density of cells. In this work, we explore density-based segmentation and tracking of cellular structures as observed in microscopy images. Using a new modified form of the Mumford-Shah energy functional, we derived a variational level-set for density-based segmentation. The novelty of the work lies in evolving an initialized contour that represents a salient structure on density maps to automatically generate novel cell structures upon convergence. We validate our methods and show results on confocal ear images of the zebrafish embryo.

An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs

In the last decade, level-set methods have been actively developed for applications in image registration, segmentation, tracking, and reconstruction. However, the development of a wide variety of level-set PDEs and their numerical discretization schemes, coupled with hybrid combinations of PDE terms, stopping criteria, and reinitialization strategies, has created a software logistics problem. In the absence of an integrative design, current toolkits support only specific types of level-set implementations which restrict future algorithm development since extensions require significant code duplication and effort. In the new NIH/NLM Insight Toolkit (ITK) v4 architecture, we implemented a level-set software design that is flexible to different numerical (continuous, discrete, and sparse) and grid representations (point, mesh, and image-based). Given that a generic PDE is a summation of different terms, we used a set of linked containers to which level-set terms can be added or deleted at any point in the evolution process. This container-based approach allows the user to explore and customize terms in the level-set equation at compile-time in a flexible manner. The framework is optimized so that repeated computations of common intensity functions (e.g., gradient and Hessians) across multiple terms is eliminated. The framework further enables the evolution of multiple level-sets for multi-object segmentation and processing of large datasets. For doing so, we restrict level-set domains to subsets of the image domain and use multithreading strategies to process groups of subdomains or level-set functions. Users can also select from a variety of reinitialization policies and stopping criteria. Finally, we developed a visualization framework that shows the evolution of a level-set in real-time to help guide algorithm development and parameter optimization. We demonstrate the power of our new framework using confocal microscopy images of cells in a developing zebrafish embryo.

An automated pipeline for cortical surface generation and registration of the cerebral cortex

The human cerebral cortex is one of the most complicated structures in the body. It has a highly convoluted structure with much of the cortical sheet buried in sulci. Based on cytoarchitectural and functional imaging studies, it is possible to segment the cerebral cortex into several subregions. While it is only possible to differentiate the true anatomical subregions based on cytoarchitecture, the surface morphometry aligns closely with the underlying cytoarchitecture and provides features that allow the surface of the cortex to be parcellated based on the sulcal and gyral patterns that are readily visible on the MR images. We have developed a fully automated pipeline for the generation and registration of cortical surfaces in the spherical domain. The pipeline initiates with the BRAINS AutoWorkup pipeline. Subsequently, topology correction and surface generation is performed to generate a genus zero surface and mapped to a sphere. Several surface features are then calculated to drive the registration between the atlas surface and other datasets. A spherical diffeomorphic demons algorithm is used to co-register an atlas surface onto a subject surface. A lobar based atlas of the cerebral cortex was created from a manual parcellation of the cortex. The atlas surface was then co-registered to five additional subjects using a spherical diffeomorphic demons algorithm. The labels from the atlas surface were warped on the subject surface and compared to the manual raters. The average Dice overlap index was 0.89 across all regions.