829 research outputs found
Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms.
Background: Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods.
Results: We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation.
Conclusions: We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss
New tools for quantitative analysis of nuclear architecture
The cell nucleus houses a wide variety of macromolecular substructures including
the cell’s genetic material. The spatial configuration of these substructures is
thought to be fundamentally associated with nuclear function, yet the architectural
organisation of the cell nucleus is only poorly understood. Advances in microscopy
and associated fluorescence techniques have provided a wealth of nuclear image
data. Such images offer the opportunity for both visualising nuclear substructures
and quantitative investigation of the spatial configuration of these objects. In this
thesis, we present new tools to study and explore the subtle principles behind nuclear
architecture.
We describe a novel method to segment fluorescent microscopy images of nuclear
objects. The effectiveness of this segmentation algorithm is demonstrated using
extensive simulation. Additionally, we show that the method performs as well as
manual-thresholding, which is considered the gold standard. Next, randomisationbased
tests from spatial point pattern analysis are employed to inspect spatial interactions
of nuclear substructures. The results suggest new and interesting spatial
relationships in the nucleus. However, this approach probes only relative nuclear
organisation and cannot readily yield a description of absolute spatial preference,
which may be a key component of nuclear architecture.
To address this problem we have developed methodology based on techniques
employed in statistical shape analysis and image registration. The approach proposes
that the nuclear boundary can be used to align nuclei from replicate images
into a common coordinate system. Each nucleus and its contents can therefore be
registered to the sample mean shape using rigid and non-rigid deformations. This
aggregated data allows inference regarding global nuclear spatial organisation. For
example, the kernel smoothed intensity function is computed to return an estimate
of the intensity function of the registered nuclear object. Simulation provides evidence
that the registration procedure is sensible and the results accurate.
Finally, we have investigated a large database of nuclear substructures using
conventional methodology as well as our new tools. We have identified novel spatial
relationships between nuclear objects that offer significant clues to their function.
We have also examined the absolute spatial configuration of these substructures
in registered data. The results reveal dramatic underlying spatial preferences and
present new and clear insights into nuclear architecture
Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement
We consider the problem of accurately identifying cell boundaries and
labeling individual cells in confocal microscopy images, specifically, 3D image
stacks of cells with tagged cell membranes. Precise identification of cell
boundaries, their shapes, and quantifying inter-cellular space leads to a
better understanding of cell morphogenesis. Towards this, we outline a cell
segmentation method that uses a deep neural network architecture to extract a
confidence map of cell boundaries, followed by a 3D watershed algorithm and a
final refinement using a conditional random field. In addition to improving the
accuracy of segmentation compared to other state-of-the-art methods, the
proposed approach also generalizes well to different datasets without the need
to retrain the network for each dataset. Detailed experimental results are
provided, and the source code is available on GitHub.Comment: 5 pages, 5 figures, 3 table
Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks
Fluorescence microscopy images usually show severe anisotropy in axial versus
lateral resolution. This hampers downstream processing, i.e. the automatic
extraction of quantitative biological data. While deconvolution methods and
other techniques to address this problem exist, they are either time consuming
to apply or limited in their ability to remove anisotropy. We propose a method
to recover isotropic resolution from readily acquired anisotropic data. We
achieve this using a convolutional neural network that is trained end-to-end
from the same anisotropic body of data we later apply the network to. The
network effectively learns to restore the full isotropic resolution by
restoring the image under a trained, sample specific image prior. We apply our
method to synthetic and real datasets and show that our results improve
on results from deconvolution and state-of-the-art super-resolution techniques.
Finally, we demonstrate that a standard 3D segmentation pipeline performs on
the output of our network with comparable accuracy as on the full isotropic
data
Accurate and versatile 3D segmentation of plant tissues at cellular resolution
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface
3D segmentations of neuronal nuclei from confocal microscope image stacks
In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario?the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei
Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
Cell segmentation in microscopy is a challenging problem, since cells are
often asymmetric and densely packed. This becomes particularly challenging for
extremely large images, since manual intervention and processing time can make
segmentation intractable. In this paper, we present an efficient and highly
parallel formulation for symmetric three-dimensional (3D) contour evolution
that extends previous work on fast two-dimensional active contours. We provide
a formulation for optimization on 3D images, as well as a strategy for
accelerating computation on consumer graphics hardware. The proposed software
takes advantage of Monte-Carlo sampling schemes in order to speed up
convergence and reduce thread divergence. Experimental results show that this
method provides superior performance for large 2D and 3D cell segmentation
tasks when compared to existing methods on large 3D brain images
DeadEasy Mito-Glia: Automatic Counting of Mitotic Cells and Glial Cells in Drosophila
Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists
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