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6 results for “Image Analysis Technique”
Dataset related to article "Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice".
<p><strong>Datasets:</strong></p> <p><strong>Table 1_Systemic parameters.xlsx </strong>- dataset related to the systemic parameters. These data are presented in Table 1. </p> <p><strong>Figure 6_Morphometric analysis.xlsx </strong>- dataset related to the morphometric characterization of the subpodocyte space and podocytes. These data are presented in Figure 6. </p> <p><strong>Abstract of the manuscript</strong></p> <p>Transmission electron microscopy (TEM) remains the gold standard for renal histopathological diagnoses, given its higher resolving power compared to light microscopy. However, it imposes several limitations on pathologists, including longer sample preparation time and a small observation area. To overcome these, we introduced a scanning electron microscopy (SEM) technique for imaging resin-embedded semi-thin sections of renal tissue. We developed a rapid tissue preparation protocol for experimental models and human biopsies which, alongside SEM digital imaging acquisition of secondary electrons (SE-SEM), enables fast electron microscopy examination, with a resolution similar to that achieved by TEM. We used this unconventional SEM imaging approach to investigate the subpodocyte space (SPS) in BTBR <em>ob/ob</em> mice with type 2 diabetes. Analysis of semi-thin sections with secondary electrons revealed that the SPS had expanded in volume and covered large areas of the glomerular basement membrane, forming wide spaces between the podocyte body and underlying filtering membrane. Our results show that SE-SEM is a valuable tool for imaging the kidney at the ultrastructural level, filling the magnification gap between light microscopy and TEM, and reveal that in diabetic mice the SPS is larger than in normal controls, which is associated with podocyte damage and impaired kidney function.</p>
High-Resolution Sporadic E Layer Observation Based on Ionosonde using a Cross-Spectrum Analysis Imaging Technique
<p>The package includes the data used in Figures 1 to 6 in the paper “High-Resolution Sporadic E Layer Observation Based on Ionosonde using a Cross-Spectrum Analysis Imaging Technique".</p> <p>Since the raw data of the ionosonde is quite large and difficult to upload, the echo data of the Es layer after high-resolution imaging processing is packed together and uploaded here.</p> <p>All data can be imported and viewed directly by MATLAB with the function of "imagesc".</p> <p>Due to internal delay, the actual range should be obtained by subtracting 5 range bins.</p>
CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques
<p>This dataset provides a collection of color images taken from the orange leaves of <em>Citrus sinensis</em> (L.) Osbeck species with diseases, nutritional deficiencies, and pest symptoms, proper to develop abnormality detection algorithms based on digital image analysis techniques. The dataset comprises 953 color images divided into 12 classes of orange leaves: Healthy, Huanglongbing (HLB), Greasy spot, Iron deficiency, Magnesium deficiency, Manganese deficiency, Nitrogen deficiency, Zinc deficiency, Texas citrus mite, Red scale, Red scale sequelae, and Citrus leafminer. Each color image was segmented by a thresholding method to obtain a binary mask of the leaf region. Samples were analyzed by the quantitative real-time polymerase chain reaction (qPCR) diagnostic test to detect the <em>Ca.</em> L. asiaticus bacterium that causes HLB disease.</p>
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
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Data and supplementary material for the paper "A simple image analysis technique for measuring bed surface texture in flume experiments"
<p>This repository contains the Matlab codes developed for the image processing illustrated in the paper "A simple image analysis technique for measuring bed surface texture in flume experiments" submitted to the Journal of Hydrology. Results of the image processing are reported in some excel spreadsheets. We also provide the pdf file of the paper "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" illustrating the laboratory experiments for which we developed the image analysis technique presented in "A simple image analysis technique for measuring bed surface texture in flume experiments". The paper titled "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" has not been published yet, but it has been accepted by the Journal of Geophysical Research - Earth Surface. Thus, we provide the editor acceptance letter as well.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.