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A Wavelet-Based Bayesian Framework for 3D Object Segmentation in Microscopy
Abstract and Figures
In confocal microscopy, target objects are labeled with fluorescent
markers in the living specimen, and usually appear with irregular
brightness in the observed images. Also, due to the existence of
out-of-focus objects in the image, the segmentation of 3-D objects in
the stack of image slices captured at different depth levels of the
specimen is still heavily relied on manual analysis. In this paper, a
novel Bayesian model is proposed for segmenting 3-D synaptic objects
from given image stack. In order to solve the irregular brightness and
out-offocus problems, the segmentation model employs a likelihood using
the luminance-invariant 'wavelet features' of image objects in the
dual-tree complex wavelet domain as well as a likelihood based on the
vertical intensity profile of the image stack in 3-D. Furthermore, a
smoothness 'frame' prior based on the a priori knowledge of the
connections of the synapses is introduced to the model for enhancing the
connectivity of the synapses. As a result, our model can successfully
segment the in-focus target synaptic object from a 3D image stack with
irregular brightness.
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In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and analysis is therefore an important task but it is still heavily reliant on manual analysis. In this paper, a novel shape modeling algorithm is proposed for automating the detection and analysis of the spots of interest. The algorithm exploits a Gaussian mixture model to characterize the spatial intensity distribution of the spots, and estimates parameters using a novel split-and-merge expectation maximization (SMEM) algorithm. In previous work the split step is random which is an issue for biological analysis where repeatability is important. The new split/merge steps are deterministic, hence more useful, and further do not impact adversely on the optimality of the final result.
mRNP granules at adult central synapses are postulated to regulate local mRNA translation and synapse plasticity. However, they are very poorly characterized in vivo. Here, in Drosophila olfactory synapses, we present early observations and characterization of candidate synaptic mRNP particles, one of which contains a widely conserved, DEAD-box helicase, Me31B. In Drosophila, Me31B is required for translational repression of maternal and miRNA-target mRNAs. A role in neuronal translational control is primarily suggested by Me31B's localization, in cultured primary neurons, to neuritic mRNP granules that contain: (i) various translational regulators; (ii) CaMKII mRNA; and (iii) several P-body markers including the mRNA hydrolases, Dcp1, and Pcm/Xrn-1. In adult neurons, Me31B localizes to P-body like cytoplasmic foci/particles in neuronal soma. In addition it is present to synaptic foci that may lack RNA degradative enzymes and localize predominantly to dendritic elements of olfactory sensory and projection neurons (PNs). MARCM clones of PNs mutant for Me31B show loss of both Me31B and Dcp1-positive dendritic puncta, suggesting potential interactions between these granule types. In PNs, expression of validated hairpin-RNAi constructs against Me31B causes visible knockdown of endogenous protein, as assessed by the brightness and number of Me31B puncta. Knockdown of Me31B also causes a substantial elevation in observed levels of a translational reporter of CaMKII, a postsynaptic protein whose mRNA has been shown to be localized to PN dendrites and to be translationally regulated, at least in part through the miRNA pathway. Thus, neuronal Me31B is present in dendritic particles in vivo and is required for repression of a translationally regulated synaptic mRNA.
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.
Many cellular functions require the synthesis of a specific protein or functional cohort of proteins at a specific time and place in the cell. Local protein synthesis in neuronal dendrites is essential for understanding how neural activity patterns are transduced into persistent changes in synaptic connectivity during cortical development, memory storage and other long-term adaptive brain responses. Regional and temporal changes in protein levels are commonly coordinated by an asymmetric distribution of mRNAs. This Review attempts to integrate current knowledge of dendritic mRNA transport, storage and translation, placing particular emphasis on the coordination of regulation and function during activity-dependent synaptic plasticity in the adult mammalian brain.
A continuous two-dimensional region is partitioned into a fine rectangular array of sites, or ‘pixels', each pixel having a particular '‘colour’ belonging to a prescribed finite set. The true colouring of the region is unknown but, associated with each pixel, there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model. The aim is to reconstruct the true scene, with the additional knowledge that pixels close together tend to have the same or similar colours. In this paper, it is assumed that the local characteristics of the true scene can be represented by a non-degenerate Markov random field. Such information can be combined with the records by Bayes' theorem and the true scene can be estimated according to standard criteria. However, the computational burden is enormous and the reconstruction may reflect undesirable large-scale properties of the random field. Thus, a simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics. The method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field. Some complications, including parameter estimation, are discussed. Potential applications are mentioned briefly.
We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
Automatic segmentation of nuclei in 3D microscopy images is essential for many biological studies including high throughput analysis of gene expression level, morphology, and phenotypes in single cell level. The complexity and variability of the microscopy images present many difficulties to the traditional image segmenta- tion methods. In this paper, we present a new method based on 3D watershed algorithm to segment such images. By using both the intensity information of the image and the geometry information of the appropriately detected foreground mask, our method is robust to intensity fluctuation within nuclei and at the same time sensitive to the intensity and geometrical cues between nuclei. Besides, the method can automatically correct potential segmentation errors by using several post-processing steps. We tested this algorithm on the 3D confocal images of C.elegans, an organism that has been widely used in biological studies. Our results show that the algo- rithm can segment nuclei in high accuracy despite the non-uniform background, tightly clustered nuclei with different sizes and shapes, fluctuated intensities, and hollow-shaped staining patterns in the images.
Robust high-breakdown-point location estimators are employed to analyze image stacks under the piecewise constant image structure model. To reduce the effect of bias along the Z-axis, the class parameters are extracted using three consecutive slices, The segmentation algorithm first determines the most reliable seed regions, which are then used in a region-growing procedure supported by local evidence. The robustness and stability of the proposed technique is shown with both synthetic and real data, the latter consisting of one MRI and one confocal microscopy set. The performance of the algorithm is consistent with the ground truth obtained with manual segmentation by physicians.
Local control of mRNA translation modulates neuronal development, synaptic plasticity, and memory formation. A poorly understood aspect of this control is the role and composition of ribonucleoprotein (RNP) particles that mediate transport and translation of neuronal RNAs. Here, we show that staufen- and FMRP-containing RNPs in Drosophila neurons contain proteins also present in somatic "P bodies," including the RNA-degradative enzymes Dcp1p and Xrn1p/Pacman and crucial components of miRNA (argonaute), NMD (Upf1p), and general translational repression (Dhh1p/Me31B) pathways. Drosophila Me31B is shown to participate (1) with an FMRP-associated, P body protein (Scd6p/trailer hitch) in FMRP-driven, argonaute-dependent translational repression in developing eye imaginal discs; (2) in dendritic elaboration of larval sensory neurons; and (3) in bantam miRNA-mediated translational repression in wing imaginal discs. These results argue for a conserved mechanism of translational control critical to neuronal function and open up new experimental avenues for understanding the regulation of mRNA function within neurons.