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A nucleus undergoing differentiation. (A-C) with increasing chromatin granularity, compared to a control somatic endothelial cell (D). Bar in A is 10 µm.
Source publication
We present nonparametric methods for segmenting and classifying stem cell nuclei so as to enable the automatic monitoring of stem cell growth and development. The approach is based on combining level set methods, multiresolution wavelet analysis, and non-parametric estimation of the density functions of the wavelet coefficients from the decompositi...
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... movies were acquired with a spinning disk microscope (Perkin Elmer) using a 40x 1.3NA Nikon objective with a resolution of 0.2 µm. We observed that nuclei in pluripotent cells were small and chromatin was generally smooth textured (Figure 2 A). During differentiation (Figure 2 B) we found that chromatin became more granular and did not vary over time, unlike pluripotent cells. ...
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... observed that nuclei in pluripotent cells were small and chromatin was generally smooth textured (Figure 2 A). During differentiation (Figure 2 B) we found that chromatin became more granular and did not vary over time, unlike pluripotent cells. By 5 weeks (Figure 2 C), differ- entiated stem cells were nearly as granular as an adult human vascular endothelial cell (Figure 2 D). ...
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... differentiation (Figure 2 B) we found that chromatin became more granular and did not vary over time, unlike pluripotent cells. By 5 weeks (Figure 2 C), differ- entiated stem cells were nearly as granular as an adult human vascular endothelial cell (Figure 2 D). Pluripotent nuclei are physically very plastic and become less compliant during dif- ferentiation due in part to chromatin condensation [10]. ...
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... differentiation (Figure 2 B) we found that chromatin became more granular and did not vary over time, unlike pluripotent cells. By 5 weeks (Figure 2 C), differ- entiated stem cells were nearly as granular as an adult human vascular endothelial cell (Figure 2 D). Pluripotent nuclei are physically very plastic and become less compliant during dif- ferentiation due in part to chromatin condensation [10]. ...
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... heterochromatin generally contains si- lenced genes, texture analysis provides a direct measure of the degree of gene silencing by chromatin remodeling. Figure 2 shows the nuclei of five cells imaged using the marker FGP-H2B in a time-lapse series of ten or eleven images over a ten minute period. ity between the images in the five classes are given in Figure 3. Blocks along the diagonal represent the intra-class dissim- ilarity, with blue and cyan blocks indicating relative homo- geneity within a class. ...
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We present a non-invasive, non-destructive automatable image-based methodology for classifying human embryonic stem cell (hESC) colonies. In contrast to differentiated colonies, pluripotent colonies contain homogeneous tight textures, thus allowing a statistical analysis of the coefficients obtained from a wavelet based texture decomposition to dis...
We propose a method for automatic segmentation of variable intensity cell nuclei in the presence of highly variable noise in fluorescence microscopy images by adding novel texture information in the wavelet domain. The proposed method calculates the Hessian matrix using the stationary wavelet transform and uses eigenvalues of the Hessian matrix to...
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Citations
... Lowry et al. [27] combined set levels, multi resolution wavelet analysis and non-parametric estimation of the density functions of the wavelet coefficients to segment and classify stem cell nuclei. The authors also used an adjustable length window to deal with small size textures where the largest inscribed rectangular window may not contain a sufficient number of pixels for multiresolution analysis of elongated and irregularly shaped nuclei. ...
... Lowry et al. [27] Used an adjustable length window to extract features using multi-resolution wavelet analysis which is used to classify the cell nuclei using a combination of set levels and non-parametric estimation of the wavelet coefficients. ...
Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson’s, Huntington’s, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting of two parts: (a) Generative Multi Adversarial Networks (GMAN) for generating synthetic images of hESC, (b) a hierarchical classification system consisting of Convolution Neural Networks (CNN) and Triplet CNNs to classify phase contrast hESC images into six different classes namely: Cell clusters, Debris, Unattached cells, Attached cells, Dynamically Blebbing cells and Apoptically Blebbing cells. The approach is totally non-invasive and does not require any chemical or staining of hESC. DeephESC 2.0 is able to classify hESC images with an accuracy of 93.23% out performing state-of-the-art approaches by at least 20%. Furthermore, DeephESC 2.0 is able to generate large number of synthetic images which can be used for augmenting the dataset. Experimental results show that training DeephESC 2.0 exclusively on a large amount of synthetic images helps to improve the performance of the classifier on original images from 93.23% to 94.46%. This paper also evaluates the quality of the generated synthetic images using the Structural SIMilarity (SSIM) index, Peak Signal to Noise ratio (PSNR) and statistical p-value metrics and compares them with state-of-the-art approaches for generating synthetic images. DeephESC 2.0 saves hundreds of hours of manual labor which would otherwise be spent on manually/semi-manually annotating more and more videos.
... The above method was also adapted to texture comparison and classification, and used to non-invasively and quantitatively classify stem cell colonies [42,43], without using chemical markers, thus preserving the colonies for use. The method is also used to classify nuclei in [44,76,77]. We will adopt this method here to compare Landsat images of regions across seasons and years, and demonstrate in the next section that it successfully predicts species turnover. ...
... For each decomposition scale, either represent the coefficients by a non-parametric histogram, or instead select a probability density function (pdf) as a model and use the coefficients to estimate the pdf's parameters. In [76], the first approach, non-parametric histograms, is used, while in [29,43,44,77], the Generalized Gaussian (GG) probability density function was selected as an appropriate model for the wavelet coefficients at each scale. The GG pdf for detail coefficients at scale s is given by: ...
... for estimating parameters v and p, including moment-matching [78] and maximum-likelihood [29,79,76]. 4. Texture joint pdf model. ...
Background:
The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.
Methodology/principal findings:
We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or [Formula: see text] diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.
Conclusions/significance:
Texture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.
... Performance of other pixel-based approaches, such as clustering [8]- [10], deteriorate quickly as the degree of distortions and irregularities observed in microscopic images increases because of lack of constraints in terms of spatial dependences. An alternative to pixel-based approaches is region-based segmentation methods [11]- [17]. Among region growing methods, watershed algorithm and its many variants [18]- [21] have been extensively used in the field of cell/nuclei segmentation for its ability to segment touching objects. ...
Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95% when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation.
... However, these approaches deal with other types of cells which do not exhibit the challenging properties of mouse ES cells as described above. On the other hand, previous approaches developed for ES cell segmentation have only been used for 2D images and for data where cell nuclei are less densely clustered (e.g., [3, 4]). ...
We present an automatic approach for 3D segmentation of mouse embryonic stem cell nuclei based on level set active contours. Due to the specific properties of these cells, standard methods for cell nucleus segmentation and splitting of cell clusters cannot be applied. Our segmentation approach combines information from two different channels which represent the nuclear region and the nuclear membrane. Moreover, we perform segmentation of gene loci within two other channels which enables single cell quantification of gene distances.
... The present approaches are: (1) chemical staining, which is rapid and consistent but destructive, rendering a portion of the colony unfit for further experimental or therapeutic use, and (2) visual inspection by a trained microscopist, which is non-invasive but time-consuming and subjective. As an alternative, we have accurately identified stem cells [6], [7], [8] using multiresolution texture analysis as a non-invasive, non-destructive pluripotency biomarker. ...
... If we assume that parameter estimates Θ, and hence posterior p k (Y | Z; Θ), are equal for both the EM and level set, we may use relation (6) to solve for the prior p k (Z = c 0 ) that causes the right hand side of (5) to be zero: ...
We presenta unifiedapproachtoExpectation-Maximization (EM) and Level Set image segmentation that combines the advantages of the two algorithms via a geometric prior that encourages local classification similarity. Compared to level sets, our method increases the information returned by providing probabilistic soft decisions, is easily extensible to multiple regions, and does not require solving Partial Differential Equations (PDEs). Relative to the basic mixture model EM, the unified algorithm improvesrobustness to noise while smoothing class transitions. We illustrate the versatility and advantages of the algorithm on two real-life problems: segmentation of induced pluripotent stem cell (iPSC) colonies in phase contrast microscopic images and information recovery from brain magnetic resonance images (MRI).
