Segmentation and intensity estimation for microarray images with saturated pixels
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ABSTRACT:
ORGANISM(S): synthetic construct Homo sapiens
PROVIDER: GSE33899 | GEO | 2011/11/23
SECONDARY ACCESSION(S): PRJNA148259
REPOSITORIES: GEO
Similar Datasets
Project description:Images and gpr files were examined using a novel saturation reduction method to determine whether accuracy could be improved by extending dynamic range of saturated pixels Three immunosignatures from human Valley Fever (Coccidiodes) patients and three immunosignatures from human influenza vaccine recipients were examined to test an algorithm that extends the apparent dynamic range of a fluorescence image. These images had several saturated spots at 70PMT and 100% laser power. The program examined the differences between Valley Fever and influenza in terms of standard image processing vs. segmentation and intensity estimation.
2011-11-23 | E-GEOD-33899 | biostudies-arrayexpress
Project description:BackgroundMicroarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2(16) - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.ResultsWe developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.ConclusionsThe presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.
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Project description:Segmentation and intensity estimation for microarray images with saturated pixels
| PRJNA148259 | ENA
Project description:Intake and absorption of cholesterol (the latter determined by double labeled cholesterol methodology) were nearly unchanged in mice fed the saturated fat diet, but the fecal excretion of neutral sterols (i.e. cholesterol and its microbial conversion products) was increased compared with control diet(+80%; p<0.01). The saturated fat diet did neither significantly affect biliary cholesterol secretion nor intestinal cholesterol absorption (49% vs. 65% in controls, double labeled water methodology, p>0.1). Thus, the increased fecal neutral sterol excretion was primarily due to increased net transintestinal cholesterol excretion (+89% versus control; p<0.05). Since a major fraction of TICE cholesterol absorption is normally reabsorbed (J Lipid Res 2019 Sep;60(9):1562-1572), the increased fecal cholesterol excretion could be due to more transintestinal excretion of cholesterol into the intestinal lumen and/or to its decreased reabsorption. The saturated fat diet increased jejunal expression of genes involved in cholesterol synthesis (Srebf2 and target genes), but did not affect whole body de novo cholesterol synthesis. Conclusion This proof-of-principle study shows that increasing the saturation of the dietary fat can stimulate fecal cholesterol excretion. Individual components of saturated fat diets are to be explored to address the responsible molecular mechanisms
2021-02-01 | GSE37097 | GEO
Project description:Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.
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Project description:Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly predict the super-resolution cell spatial profiles from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk, which is validated on independent longitudinal cohorts. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.
2025-01-29 | GSE287979 | GEO
Project description:Cryo-electron microscopy is an experimental technique that is able to produce 3D gray-scale images of protein molecules. In contrast to other experimental techniques, cryo-electron microscopy is capable of visualizing large molecular complexes such as viruses and ribosomes. At medium resolution, the positions of the atoms are not visible and the process cannot proceed. The medium-resolution images produced by cryo-electron microscopy are used to derive the atomic structure of the proteins in de novo modeling. The skeletons of the 3D gray-scale images are used to interpret important information that is helpful in de novo modeling. Unfortunately, not all features of the image can be captured using a single segmentation. In this paper, we present a segmentation-free approach to extract the gray-scale curve-like skeletons. The approach relies on a novel representation of the 3D image, where the image is modeled as a graph and a set of volume trees. A test containing 36 synthesized maps and one authentic map shows that our approach can improve the performance of the two tested tools used in de novo modeling. The improvements were 62 and 13 percent for Gorgon and DP-TOSS, respectively.
| S-EPMC4104753 | biostudies-literature
Project description:This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively.
| S-EPMC4679509 | biostudies-literature