Multi-Scale Imaging and Informatics Pipeline for In Situ Pluripotent Stem Cell Analysis

Human pluripotent stem (hPS) cells are a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells cultured in vitro exhibit a high degree of heterogeneity, presenting an obstacle to clinical translation. hPS cells grow in spatially patterned colony structures, necessitating quantitative single-cell image analysis. We offer a tool for analyzing the spatial population context of hPS cells that integrates automated fluorescent microscopy with an analysis pipeline. It enables high-throughput detection of colonies at low resolution, with single-cellular and sub-cellular analysis at high resolutions, generating seamless in situ maps of single-cellular data organized by colony. We demonstrate the tool's utility by analyzing inter- and intra-colony heterogeneity of hPS cell cycle regulation and pluripotency marker expression. We measured the heterogeneity within individual colonies by analyzing cell cycle as a function of distance. Cells loosely associated with the outside of the colony are more likely to be in G1, reflecting a less pluripotent state, while cells within the first pluripotent layer are more likely to be in G2, possibly reflecting a G2/M block. Our multi-scale analysis tool groups colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image processing techniques. Overall, the imaging informatics pipeline presented offers a novel approach to the analysis of hPS cells that includes not only single cell features but also colony wide, and more generally, multi-scale spatial configuration

Enhancing the surgeons reality : Smart visualization of bolus time of arrival and blood flow anomalies from time lapse series for safety and speed of

A noise adaptive Cusum-based algorithm for determining the arrival times of contrast at each spatial location in a 2D time sequence of angiographic images is presented. We employ a new group-wise registration algorithm to remove the effect of patient motions during the acquisition process. By using the registered image the proposed arrival time provides accurate results without relying on a priori knowledge of the shape of the time series at each location or even on the time series at each location having the same shape under translation.Charles Stark Draper Laborator

Spatio-Temporal Data Fusion for 3D+T Image Reconstruction in Cerebral Angiography

This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss-Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.National Institutes of Health (U.S.)Charles Stark Draper Laboratory (Internal Research and Development funds)Cam Neely Foundation for Cancer CareNational Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant 1 R01 EB006161-01A2)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant 1 R21 HL102685-01

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

<div><h3>Background</h3><p>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.</p> <h3>Methodology/Principal Findings</h3><p>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 diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or 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.</p> <h3>Conclusions/Significance</h3><p>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.</p> </div

Remote-sensed images for the Arthur R. Marshall Loxahatchee National Wildlife Refuge (WCA 1) during the dry season for the period 1987–2011.

<p>The first three years (1984–1986) images are not represented. The representative region in which the texture analysis is performed is delineated in red for each image. The red regions are characterized by a cloud cover lower than 20%. The green regions identify where the data of species are available. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616.s001" target="_blank">Figure S1</a> reports the images for the wet season.</p

Estimated and measured local species-richness.

<p>The local species-richness ( diversity) and the estimated local species-richness (i.e. the Shannon entropy of the green band, using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616.e106" target="_blank">Equation 5</a>) are reported in plot (a) and (b) from 1984 to 2011 respectively. In plot (c) the functional relationship between the Shannon entropy of the green band and the local species-richness for WCA 1 is reported. The inset, which reports the normalized entropy, shows the ability of the Shannon entropy to capture at least 70% of the measured local species-richness. This percentage is 80% and 77% in the dry and wet season respectively. The dashed grey curves are the 95% confidence interval of the linear regression exponent. Variabilities of measured exponents are found by bootstrapping over points and deriving slopes by the linear and the Jackknife models <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616-Warton1" target="_blank">[92]</a>.</p

Interannual entropy of WCA 1 Landsat images and rainfall in the period 1984–2011 considered.

<p>(a, b) Shannon entropy of the representative regions of WCA 1 in the dry and in the wet season respectively (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone-0046616-g001" target="_blank">Figure 1</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616.s001" target="_blank">Figure S1</a>) for the red, green, and blue bands. The Shannon entropy for the green bad is proportional to the diversity of plant species. (c) Average annual rainfall (in mm) in the dry (red line) and wet season (blue line).</p

Graphical explanation of the analysis performed for the WCA 1.

<p>Landsat images (from 1984 to 2011) are acquired for the dry and the wet season. The example is reported for the years 1997 and 1998. The changes in vegetation composition are analyzed using and diversity among seasons and among years. For the interseasonal analysis the time-scale is on average six months between seasons of the same year, while for the interannual analysis the time-scale is about a year between the same season of different years. and diversity from data are compared to the estimates of the Shannon entropy (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616.e106" target="_blank">Equation 5</a>) and of the KL divergence (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046616#pone.0046616.e083" target="_blank">Equation 4</a>) for the green band of the images respectively.</p