Showing papers by "Olaf Ronneberger published in 2012"

Vertebrate kidney tubules elongate using a planar cell polarity-dependent, rosette-based mechanism of convergent extension.

Abstract: Gerd Walz and colleagues use in vivo imaging in Xenopus laevis embryos and show that kidney tubule elongation occurs by a multicellular rosette-based mechanism, which has previously only been observed in Drosophila melanogaster. These data show that rosette-based cell intercalation is a highly conserved cellular mechanism during epithelial morphogenesis.

ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains

Abstract: Precise three-dimensional (3D) mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development We developed the Virtual Brain Explorer (ViBE-Z), a software that automatically maps gene expression data with cellular resolution to a 3D standard larval zebrafish (Danio rerio) brain ViBE-Z enhances the data quality through fusion and attenuation correction of multiple confocal microscope stacks per specimen and uses a fluorescent stain of cell nuclei for image registration It automatically detects 14 predefined anatomical landmarks for aligning new data with the reference brain ViBE-Z performs colocalization analysis in expression databases for anatomical domains or subdomains defined by any specific pattern; here we demonstrate its utility for mapping neurons of the dopaminergic system The ViBE-Z database, atlas and software are provided via a web interface

Shroom3 is required downstream of FGF signalling to mediate proneuromast assembly in zebrafish

Abstract: During development, morphogenetic processes require a precise coordination of cell differentiation, cell shape changes and, often, cell migration. Yet, how pattern information is used to orchestrate these different processes is still unclear. During lateral line (LL) morphogenesis, a group of cells simultaneously migrate and assemble radially organized cell clusters, termed rosettes, that prefigure LL sensory organs. This process is controlled by Fibroblast growth factor (FGF) signalling, which induces cell fate changes, cell migration and cell shape changes. However, the exact molecular mechanisms induced by FGF activation that mediate these changes on a cellular level are not known. Here, we focus on the mechanisms by which FGFs control apical constriction and rosette assembly. We show that apical constriction in the LL primordium requires the activity of non-muscle myosin. We demonstrate further that shroom3, a well-known regulator of non-muscle myosin activity, is expressed in the LL primordium and that its expression requires FGF signalling. Using gain- and loss-of-function experiments, we demonstrate that Shroom3 is the main organizer of cell shape changes during rosette assembly, probably by coordinating Rho kinase recruitment and non-muscle myosin activation. In order to quantify morphogenesis in the LL primordium in an unbiased manner, we developed a unique trainable 'rosette detector'. We thus propose a model in which Shroom3 drives rosette assembly in the LL downstream of FGF in a Rho kinase- and non-muscle myosin-dependent manner. In conclusion, we uncovered the first mechanistic link between patterning and morphogenesis during LL sensory organ formation.

Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy

Abstract: We propose an algorithm for 3-D multiview deblurring using spatially variant point spread functions (PSFs). The algorithm is applied to multiview reconstruction of volumetric microscopy images. It includes registration and estimation of the PSFs using irregularly placed point markers (beads). We formulate multiview deblurring as an energy minimization problem subject to L1-regularization. Optimization is based on the regularized Lucy-Richardson algorithm, which we extend to deal with our more general model. The model parameters are chosen in a profound way by optimizing them on a realistic training set. We quantitatively and qualitatively compare with existing methods and show that our method provides better signal-to-noise ratio and increases the resolution of the reconstructed images.

Fast Rotation Invariant 3D Feature Computation Utilizing Efficient Local Neighborhood Operators

Abstract: We present a method for densely computing local rotation invariant image descriptors in volumetric images. The descriptors are based on a transformation to the harmonic domain, which we compute very efficiently via differential operators. We show that this fast voxelwise computation is restricted to a family of basis functions that have certain differential relationships. Building upon this finding, we propose local descriptors based on the Gaussian Laguerre and spherical Gabor basis functions and show how the coefficients can be computed efficiently by recursive differentiation. We exemplarily demonstrate the effectiveness of such dense descriptors in a detection and classification task on biological 3D images. In a direct comparison to existing volumetric features, among them 3D SIFT, our descriptors reveal superior performance.

2D/3D rotation-invariant detection using equivariant filters and kernel weighted mapping

Abstract: In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or brute-force learning, neglecting the intrinsic properties of rotations. In this paper, we present a rotation invariant detection approach built on the equivariant filter framework, with a new model for learning the filtering behavior. The special properties of the harmonic basis, which is related to the irreducible representation of the rotation group, directly guarantees rotation invariance of the whole approach. The proposed kernel weighted mapping ensures high learning capability while respecting the invariance constraint. We demonstrate its performance on 2D object detection with in-plane rotations, and a 3D application on rotation-invariant landmark detection in microscopic volumetric data.

Influence of cell-to-cell variability on spatial pattern formation

Abstract: Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.

Blind deconvolution with PSF regularization for wide-field microscopy

Abstract: We propose to use a kernel intensity penalizer (KIP) in the blind maximum likelihood expectation maximization (MLEM) deconvolution scheme. With this very general kernel regularization term, we can stabilize the blind MLEM scheme even for the deconvolution of wide-field microscopic recordings. No complex prior point spread function models are needed. We combine state of the art optimization schemes using Tikhonov-Miller and TV regularization with our new kernel regularization. The proposed method improves the conventional deconvolution methods in terms of SNR on real and simulated datasets.

Modeling of Sparsely Sampled Tubular Surfaces Using Coupled Curves

Abstract: We present a variational approach to simultaneously trace the axis and determine the thickness of 3-D (or 2-D) tubular structures defined by sparsely and unevenly sampled noisy surface points Many existing approaches try to solve the axis-tracing and the precise fitting in two subsequent steps In contrast to this our model is initialized with a small cylinder segment and converges to the final tubular structure in a single energy minimization using a gradient descent scheme The energy is based on the error of fit and simultaneously penalizes strong curvature and thickness variations We demonstrate the performance of this closed formulation on volumetric microscopic data sets of the Arabidopsis root tip, where only the nuclei of the cells are visible