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71 results for “simulated images”
Data for Matlab package locFISH to simulate realistic 3d smFISH images
<p>Different data-sets needed by the Matlab package locFISH. locFISH allows the simulation and analysis of realistic single molecule FISH (smFISH) images.</p> <p><strong>data_simulation.zip</strong><br> Contains all necessary data to simulated smFISH images. Specifically, the zip archive contains a library of 3D cell shapes, realistic imaging background, and a simulated PSF (Point Spread Function). </p> <p><strong>GAPDH.zip</strong><br> Contains the smFISH data of GAPDH and the corresponding analysis results, which were used to create the library of cell shapes provided in data_simulation.zip </p> <p>For more details on these data and how do to use them, please consult the detailed user-manual provided with <strong>locFISH</strong>, available at</p> <p>https://bitbucket.org/muellerflorian/fish_quant</p>
Data, analysis scripts, simulations, and schematics files for "Position Sensitive Alpha Detector for an Associate Particle Imaging System"
<p>Measured data and analysis script, as well as, simulated data, input scripts, as well as, schematics for the readout board for our publication "Position Sensitive Alpha Detector for an Associate Particle Imaging System"</p>
Dataset of 100 simulated root images, with different resolutions
<p>All images were analysed with WinRhizo<sup>TM</sup> 2013, Regent method (Régent Instruments Inc 2013) and ImageJ (1.51j8, Schneider, Rasband, & Eliceiri, 2012) with the macro IJ_Rhizo (IJ_Rhizo_v0beta). We conducted batch analyses with the grey level thresholds set to automatic at all four resolutions (200, 400, 800 and 1200 DPI). We further analysed the images at 800 dpi and 1200 dpi with the threshold value manually set to 144. We considered this value suitable after visual inspection of 10 images at different thresholds using the threshold slider of WinRhizo<sup>TM</sup>. Since no debris was simulated, we used no automatic debris correction. For WinRhizo<sup>TM</sup>, the boundaries of diameter classes were set from 0.1 to 1.9 mm (with an increment of 0.1 mm).</p> <p>The dataset contains:</p> <p>- the root images in jpg format (images.zip)</p> <p>- the root architecture data in rsml format (rsml.zip)</p> <p>- the ground-truth value for each image (all_root_data_2D.csv)</p> <p>- the ground-truth value for each root in each image (ground_truth_data_2D.csv)</p> <p>- the estimation given by WinRhizo and IJ_Rhizo (all_analyses_2D.txt and all_analyses_fixed.txt)</p> <p>- the different codes used for the analysis (*.R)</p> <p>Dataset used in : Accuracy of image analysis tools for functional root traits: A comment on Delory et al., 2017 from Rose and Lobet (2019), Methods in Ecology and Evolution. </p> <p>The images in this dataset are a subset of the dataset here: https://doi.org/10.5281/zenodo.208214</p>
Dataset for "LOROS: Laboratory Simulations of the Optical RadiOmeter composed of CHromatic Imagers (OROCHI) Experiment of the Martian Moons eXploration (MMX) Mission"
<p>This dataset hosts the image and numerical data analysed and derived in the accompanying Stabbins & Kameda article for the special issue of Progress in Earth and Planetary Science on instrumentation and preparations for the JAXA Martian Moons eXploration (MMX) mission. The paper describes and validates the performance of the Laboratory OROCHI Simulator (LOROS).</p> <p>OROCHI (Optical RadiOmeter composed of CHromatic Imagers) is a multispectral multi-view imaging system for the JAXA MMX spacecraft, that will image Phobos and Deimos across 8 visible and near-infrared spectral channels with unprecedented spatial resolution, recording data that in synergy with the other instruments of the MMX spacecraft and rover will constrain hypotheses on the origin of the Martian moons.</p> <p>LOROS is a laboratory simulator of OROCHI, constructed from commercial off-the-shelf parts.</p> <p>The dataset for the characterisation and validation of LOROS is composed of the following sub-sets:</p> <p>A. Modulation Transfer Function<br>B. Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon<br>C. Radiometric Calibration<br>D. Dark Spectralon Validation</p> <div> <h2>Dataset A: Modulation Transfer Function</h2> This dataset includes the table of results of MTF measurements of the slant-edge target at 5 different random orientations in the range of ~7--10°: <div>- <code>mtf_results_07122023.csv</code></div> <br> <div>and the region-of-interest images, for each orientation and each LOROS channel, used to perform the analysis via the <a href="https://sourceforge.net/p/mtfmapper/home/Home/" target="_blank" rel="noopener">MTF Mapper software</a>:</div> <div>- <code>mtf_measurements_07122023</code></div> <br> <div>The directory tree of measurements, for the <em>n</em>th orientation, is illustrated below. Region-of-interest images are stored under <code>img</code>, and are averaged over 25 repeat images to minimise random noise, have had dark frames subtracted, and have been converted from 12-bit to 8-bit grayscale images for compatibility with the MTF Mapper software. Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR) diagnostics generated by MTF Mapper are stored in the <code>results</code> directory.</div> <div> </div> <div><code>mtf_measurements_07122023</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── 0_850_img_ave.tif</code></div> <div><code>│ │ ├── 1_475_img_ave.tif</code></div> <div><code>│ │ ├── ...</code></div> <div><code>│ ├── results</code></div> <div><code>│ │ ├── 0_850_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_mtf_values.txt</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_sfr_values.txt</code></div> <div><code>│ │ ├── 1_475_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── ...</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n+1*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── ...</code></div> <div> </div> <div>This data constitutes part of <strong>Table 1</strong> and <strong>Figure 2</strong> of the manuscript.</div> <div> <h2>Dataset B: Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon</h2> This dataset includes the high-resolution ($\delta\lambda$=1 nm) reference reflectance spectra of the representative Carbonaceous Chondrite meteorite (<a href="https://westernreflectancelab.com/visor/graph/?results-selection=16136&results-item=16136&results-item=15972&results-item=231&results-item=230&graph=&form-TOTAL_FORMS=1&form-INITIAL_FORMS=0&form-MIN_NUM_FORMS=0&form-MAX_NUM_FORMS=1000&form-0-sample_name=nogoya&form-0-any_field=meteorite&form-0-id=&sort_params=-sample_name&page_selected=1&jump-to-page=" target="_blank" rel="noopener">Nogoya)</a> and the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>):<br> <div>- <code>highres_input.csv</code></div> <br> <div>and the resampled spectra of these materials expected for OROCHI and LOROS filter wavelengths:</div> <br> <div>- <code>loros_observation.csv</code></div> <div>- <code>orochi_observation.csv</code></div> <br> <div><code>B_expected_reflectance</code></div> <div><code>├── README.md</code></div> <div><code>├── highres_input.csv</code></div> <div><code>├── loros_observation.csv</code></div> <div><code>└── orochi_observation.csv</code></div> <br> <div>This data constitutes <strong>Table 1</strong> and <strong>Figure 10</strong> of the manuscript.</div> <div> </div> <div> <div> <h2>Dataset C: Radiometric Calibration</h2> This dataset contains the image and derived data for 4 experiments with different illumination conditions for characterising the radiometric response of each of the 8 channels of LOROS.</div> <div><br> <div>This dataset contributes to <strong>Tables 2 - 4</strong> and <strong>Figures 3 - 9</strong> of the manuscript.</div> <br> <div>The final derived metrics are hosted in the spreadsheet:</div> <br> <div>- <code>measured_sensor_properties.csv</code></div> <br> <div>and image data and intermediary derived properties for each experiment are stored in the</div> <br> <div>- <code>experiments</code></div> <br> <div>directory.</div> <br> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ ├── F*S99L10</code></div> <div><code>│ ├── FGS99L2</code></div> <div><code>│ └── FGS99L10</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <br> <h3><code>experiments</code> Directories</h3> In the directory of each experiment are sub-directories hosting Photon Transfer and Dark Transfer datasets, and a spreadsheet of derived metrics of these.<br> <div> </div> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ │ ├── dark_transfer_curve</code></div> <div><code>│ │ ├── photo_transfer_curve</code></div> <div><code>│ │ └── F*S5L10_derived_properties.csv</code></div> <div><code>│ └── ...</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <div> </div> </div> <div> </div> <div><strong>Derived Properties</strong><br> <div> </div> <div>The spreadsheet (<code>[experiment]_derived_properties.csv</code>) collecting the properties derived from each experiment holds the following information, that has been extracted from the Photon Transfer and Dark Transfer curves as described in §4.2 of the manuscript:</div> <br> <div><code>camera # The camera number and wavelength</code></div> <div><code>k_adc # Sensitivity (e-/DN)</code></div> <div><code>full_well_e # Saturation Capacity (electrons)</code></div> <div><code>full_well_dn # Saturation Capacity (Digital Numbers)</code></div> <div><code>read_noise_e # Read Noise (electrons)</code></div> <div><code>read_noise_dn # Read Noise (Digital Numbers)</code></div> <div><code>bias_e # Offset (electrons)</code></div> <div><code>bias_dn # Offset (Digital Numbers)</code></div> <div><code>dark_current_e # Dark Current (electrons/second)</code></div> <div><code>dark_current_dn # Dark Current (Digital Numbers/second)</code></div> <div><code>DR # Dynamic Range</code></div> <div><code>lin_min # Minimum Linearity Error</code></div> <div><code>lin_max # Maximum Linearity Error</code></div> <div><code>linearity # Average Linearity Error</code></div> <div><code>snr_max # Maximum Signal-to-Noise Ratio</code></div> <div><code>t_exp_min # Minimum Exposure used in experiment (seconds)</code></div> <div><code>t_exp_max # Maximum Exposure used in experiment (seconds)</code></div> <div><code>expected_response # Expected Response (or 'Digital Flux') for OROCHI^12 at Phobos (Digital Numbers/second)</code></div> <div><code>response # Fitted Response (or 'Digital Flux') (Digital Numbers/second)</code></div> <br> <div>These values are given for each channel of LOROS, as well as the expected values for LOROS in off-the-shelf configuration (with no gain adjustment), LOROS with the gain adjustment, and OROCHI if downsampled to 12-bit resolution digital numbers.</div> <br> <div>This data constitutes <strong>Table 2</strong> of the manuscript.</div> <br> <div><strong>Dark Transfer Curve</strong></div> <br> <div>The <code>dark_transfer_curve</code> directory hosts the derived Dark Transfer Curve data (<code>derived_data</code>) and the source region-of-interest dark image pair data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>dark_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_dtc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_dtc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_dtc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_dtc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_dtc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_dtc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_dtc.csv</code></div> <div><code>│ └── F*S5L10_7_550_dtc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_10095570us_1_calibration.tif</code></div> <div><code>│ ├── 850_10095570us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts a dark image pair for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The dark transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_dtc</code>) gives the data derived from each raw image data, with the following values:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value of the region of interest</code></div> <div><code>std_t # total standard deviation of the region of interest</code></div> <div><code>std_rs # read+shot-noise standard deviation, copmuted from the difference of the image pair</code></div> <br> <div>This data constitutes <strong>Figures 5 and 8</strong> of the manuscript.</div> <br> <div><strong>Photon Transfer</strong></div> <br> <div>The <code>photon_transfer_curve</code> directory hosts the derived Photon Transfer Curve data (<code>derived_data</code>) and the source region-of-interest illuminated image pairs and associated dark frame image data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>photo_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_ptc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_ptc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_ptc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_ptc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_ptc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_ptc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_ptc.csv</code></div> <div><code>│ └── F*S5L10_7_550_ptc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_d_drk.tif</code></div> <div><code>│ ├── 850_105828us_1_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts an image pair and dark frame for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The photon transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_ptc</code>) gives the data derived from each raw image data, with the following values across the region-of-interest:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value (Digital Numbers)</code></div> <div><code>std_t # total standard deviation (Digital Numbers)</code></div> <div><code>std_rs # read+shot-noise standard deviation (Digital Numbers), computed from the difference of the image pair</code></div> <div><code>d_mean # average value of the dark (Digital Numbers)</code></div> <div><code>d_dsnu # Dark Signal Nonuniformity (Digital Numbers)</code></div> <div><code>std_s # Shot Noise (read noise removed) (Digital Numbers)</code></div> <div><code>k_adc # Sensitivity (note this the point-wise sensitivity, rather than fitted) (electrons/Digital Number)</code></div> <div><code>linearity # Linearity Error (point-wise distance to least-squares linear fit) (%)</code></div> <div><code>snr # Signal-to-Noise Ratio, derived from shot-noise (point-wise)</code></div> <div><code>snr_t # Signal-to-Noise Ratio, derived from total noise (point-wise)</code></div> <div><code>e- # Electron count, derived from sensitivity</code></div> <div><code>e-_noise # Electron shot-noise, derived from sensitivity</code></div> <br> <div>This data constitutes <strong>Figures 3, 4, 6, 7 & 9</strong> of the manuscript.</div> <br> <div><strong>Measured Sensor Properties</strong></div> <br> <div>The <code>measured_sensor_properties.csv</code> spreadsheet collects and averages the following metrics over the 4 experiments performed, to give the values for each channel, along with the expected values for LOROS in off-the-shelf configuration, gain-adjusted LOROS, and OROCHI downsampled to 12-bit resolution.</div> <br> <div><code>SNR Max</code></div> <div><code>Dynamic Range (dB)</code></div> <div><code>Dynamic Range (bits)</code></div> <div><code>Sensitivity (e-/DN)</code></div> <div><code>Saturation Capacity (e-)</code></div> <div><code>Saturation Capacity (DN)</code></div> <div><code>Read Noise (e-)</code></div> <div><code>Read Noise (DN)</code></div> <div><code>Nonlinearity (%)</code></div> <div><code>Dark Signal@30°C (e-/s)</code></div> <div><code>Dark Signal@30°C (DN/s)</code></div> <div><code>Bias (e-)</code></div> <div><code>Bias (DN)</code></div> <div><code>DSNU1288 (DN)</code></div> <div><code>DSNU1288 (e-)</code></div> <div><code>PRNU1288 (%)</code></div> <br> <div>This data constitutes <strong>Table 3</strong> of the manuscript.</div> <div> </div> <div> <h2>Dataset D: Dark Spectralon Validation</h2> This dataset contains the raw image and derived data used to demonstrate the ability of LOROS to measure the spectral reflectance of the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>).<br> <div>The image data is hosted in the directory:</div> <br> <div>- <code>raw_data</code></div> <br> <div>and the processed data (e.g. reflectance products) are hosted in the directory:</div> <br> <div>- <code>processed_data</code></div> <br> <div><code>D_dark_spectralon_validation</code></div> <div><code>├── processed_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ └── SCT99</code></div> <div><code>├── raw_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ ├── SCT5_dark</code></div> <div><code>│ ├── SCT99</code></div> <div><code>│ └── SCT99_dark</code></div> <div><code>└── README.md</code></div> <br> <div><strong>Raw Data</strong></div> <br> <div>The raw data directory contains images captured of <code>SCT5</code> and <code>SCT99</code> (99% reflectance white Spectralon), and accompanying dark frames, hosted in the <code>SCT5_dark</code> and <code>SCT99_dark</code> frames respectively.</div> <br> <div>For each channel, 25 repeat images have been captured for the illuminated and dark frames.</div> <br> <div><strong>Processed Data</strong></div> <br> <div>The processed SCT99 and SCT5 datasets differ slightly. Both include:</div> <br> <div><code>├── img</code></div> <div><code>├── rfl</code></div> <div><code>└── rois</code></div> <br> <div>directories, with the SCT99 scene also including a <code>cal</code> directory.</div> <br> <div><code>img</code> hosts a set of <code>context</code> figures, showing the regions of interest selected, <code>fits</code> hosts the floating point mean (<code>ave</code>), standard error (<code>err</code>), standard deviation (<code>std</code>) and single-frame (<code>one</code>), all in units of Digital Number, after dark frame subtraction, flat-fielding and linearity correction. <code>uint8</code> hosts the same data rescaled to 8-bit resolution, for quick-view.</div> <br> <div><code>rfl</code> hosts the same set as <code>img</code>, after conversion to units of reflectance against the results of the SCT99 calibration (see §3.5 of the manuscript).</div> <br> <div><code>rois</code> gives plots of the mean and error of the reflectance spectrum of the region of interest, as well as the Signal-to-Noise Ratio, as well as the data for each region-of-interest (<code>roi_data</code>).</div> <br> <div><code>cal</code> also gives context figures for each channel region-of-interest, as converted to units of reflectance coefficients (1/DN/s).</div> </div> </div> </div> </div> </div>
XMM-Newton imaging simulations for STATiX characterization
<p>Here we distribute 1000 imaging simulations of XMM-Newton observations containing transient X-ray sources, used for the characterization of the STATiX source detection pipeline (Ruiz et al., in preparation). SIXTE (SImulation of X-ray TElescopes; Dauser et al. 2019) is used to simulate X-ray imaging observations of the EPIC-PN camera onboard XMM-Newton. Each simulation consists of an event list file and an attitude file. These simulations were produced using the Python code available at https://github.com/ruizca/sixtexmm.</p>
Simulation of hyperspectral imaging recordings of the human exposed cortex
<p>This dataset contains the results of the simulation of a hyperspectral imaging recording of the exposed cortex of a human brain. Monte-Carlo (MC) simulations of the propagation of visible and near-infrared light in the exposed cortex of human were carried out, and the results of the MC simulation were processed to produce an hypercube, i.e., a set of images at various wavelength. Here, the light propagation of 41 wavelengths from 500 to 900nm were simulated and the images reconstructed.</p> <p>The simulation was based on the MCX software (<a href="https://github.com/fangq/mcx).%20">https://github.com/fangq/mcx). </a>The parameters of the simulations (at every wavelength) can be found in the ConfigurationFiles.zip file (1 per wavelength, named: cfg_WMC_wavelengthNumber). For the full details of the files structure, see: https://github.com/fangq/mcx.</p> <p>The domain used for the simulations was generated from an RGB image of the exposed cortex acquired during a surgical procedure. The image was segmented with a semi-automated procedure into three classes: gray matter, large blood vessel, and capillaries. Pixels were clustered into ten clusters using the K-means algorithm from the python library OpenCV (v4.8.0). The components of each cluster were manually sorted and attributed to the three classes. Three functional regions were defined as 1cm disk based on electrical brain stimulation findings, which lead to three other classes: activated grey matter, activated large blood vessel and activated capillaries.</p> <p>Once the image segmented into six classes, the brain volume was modelled. The binary segmentation masks were replicated along the z axis on 2cm to avoid any photon loss. Then the blood vasculature was modelled using morphological erosion. The structuring element used for the erosion was set to 0 (in pixels) for z=0 (in pixels) and was increased of 1 pixel while increasing z axis. The binary volumes of the six classes were finally merged together with a final isotropic resolution of 75um.</p> <p>The Hypercubes.mat file contains the results of the reconstructed ideal images at the surface of the brain, as calculated in reference [1], with a resolution of 116 x 116 pixels (0.5285 x 05285 mm). The absorption properties considered for this reconstruction are available in the ConfigurationFiles.zip file (variable: mua_WMC).</p> <p>The variables of the Hypercube.mat file are :</p> <ul> <li>Reflectance: the reflectance matrix (116 pixel x 116 pixels x 41 wavelengths).</li> <li>Wavelength: the wavelength vector (41 wavelengths)</li> <li>Mean_path_length: the image of the mean photon pathlength for each pixel and each wavelength (116 pixel x 116 pixels x 41 wavelengths)</li> </ul> <p>Reference:</p> <p>[1] Yao, R., Intes, X. and Fang, Q., “Direct approach to compute Jacobians for diffuse optical tomography using perturbation Monte Carlo-based photon ‘replay,’” Biomed. Opt. Express 9(10), 4588 (2018).</p> <p> </p>
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
<p><span>Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely <a name="_Hlk56106607">distinguishable</a>. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is <a name="_Hlk48748756">based on the state-of-the-art U-Net</a>, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. </span></p> <p><span>Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.</span></p>
Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems
<p>The data was collected, simulated and analyzed in the framework of the CassavaStore project (a collaboration between IBG-2, Forschungszentrum Jülich, Germany and different institution from Thailand; for details see <a href="https://www.international-bioeconomy.org/cassavastore_eng">https://www.international-bioeconomy.org/cassavastore_eng</a>). Aim of this project is to get a better understanding of storage root development in cassava (<em>Manihot esculenta</em> Crantz) in order to optimize cassava growth with respect to variety breeding and growth management. The storage root is one of the main providers of starch in Thailand and therefore of high economic importance. Monitoring the formation of storage roots over time via quantification of the root diameter distribution of excavated root systems was one of the key aspects addressed in this project. To measure the diameters a software was developed that identifies roots in RGB images and analyzes the diameters along each identified root automatically. The published data contains 1) analyzed images from cassava roots that were acquired in a video box; 2) simulated virtual root model images with known root diameter distributions that were used to validate the analysis approach; 3) a description of the data and the folder structure.</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Data related to: Simulated TEM imaging of a heavily irradiated metal
<p>Data set related to article "Simulated TEM imaging of a heavily irradiated metal"</p> <p>The data is the output of a simulation of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (1 dpa). The simulation was done in LAMMPS, only every 100th snapshot in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of 0.000167 dpa, i.e. frame 1000 corresponds to a dose of 0.167 dpa. The files are zipped. A finer resolution can be supplied upon request.</p> <p>The simulations were performed for the article: <a href="https://doi.org/10.1103/PhysRevMaterials.6.063601" target="_blank" rel="noopener">10.1103/PhysRevMaterials.6.063601</a>, where more details on the simulations are found. The data is here made available for the article: <a href="https://doi.org/10.48550/arXiv.2401.14781" target="_blank" rel="noopener">10.48550/arXiv.2401.14781</a>.</p> <p>The TEM simulation software is available at: <a href="https://github.com/mason-daniel/simulated_tem">https://github.com/mason-daniel/simulated_tem</a>.</p>
Spectroscopic and Interferometric Sum-Frequency Imaging of Strongly Coupled Phonon Polaritons in SiC Metasurfaces - Experimental and Simulation Data
<p>Data repository supporting the manuscript 'Spectroscopic and Interferometric Sum-Frequency Imaging of Strongly Coupled Phonon Polaritons in SiC Metasurfaces'.</p>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
Simulation codes for the transition from monomorphism to dimorphism in mirror-image flowers
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Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
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Simulated STEM and HRTEM images of sheared β" precipitates
<p>The simulated scanning transmission electron microscopy (STEM) and high-resolution transmission electron microscopy (HRTEM) data in this repository form the basis for the publication "". They are simulated using the <a href="https://github.com/Ivanlh20/MULTEM">MULTEM</a> package under the multislice and frozen phonon approximations. The data is provided as HDF files readable through the open source Python package <a href="https://hyperspy.org/">HyperSpy.</a> Blind source separation results of a principal component analysis (PCA) of two through-thickness HRTEM fast-Fourier image stacks are also provided as png images (MULTEM0101_FFT_Power_spectrum_RPCA_GoDec_bss and MULTEM0120_FFT_Power_spectrum_RPCA_GoDec_bss). These results can be obtained by performing a PCA decomposition followed by a blind source separation (with two components) of the respective data in e.g. HyperSpy.</p> <p>Each simulation is given the generic label "MULTEMxxxx" and is performed on a specific atomistic model of a beta'' precipitate (present in ternary Al-Mg-Si alloys at peak-hardness). The different models and simulations relate to specific shearing configurations of the precipitates, which has been modelled as a rigid translation of the precipitate structure on a certain plane in a certain direction. The rigid translations usually correspond to a combination of one of the slip systems in fcc aluminium (i.e. a relative shift of a <1/2 1/2 0> Burgers vector on a {111} slip plane), but in some cases a (001) slip plane is investigated instead. While the preciptiate itself is rigidly sheared, the surrounding matrix is relaxed using molecular dynamics to imitate the strain field surrounding these precipitate structures.</p> <p>Information about the models themselves are accessible through the metadata of each file as well as provided in this repository (see below). The shear configuration of the models are as follows:</p> <ul> <li>MULTEM0101: Not sheared</li> <li>MULTEM0102: 1x[-1-10]/(001) - A</li> <li>MULTEM0103: 1x[-101]/(111) - A</li> <li>MULTEM0104: 2x[-1-10]/(001) - A</li> <li>MULTEM0105: 2x[-101]/(111) - A</li> <li>MULTEM0106: 3x[-1-10]/(001) - A</li> <li>MULTEM0107: 3x[-101]/(111) - A</li> <li>MULTEM0108: 5x[-1-10]/(001) - A</li> <li>MULTEM0109: 5x[-101]/(111) - A</li> <li>MULTEM0110: 10x[-1-10]/(001) - A</li> <li>MULTEM0111: 10x[-101]/(111) - A</li> <li>MULTEM0112: 1x[1-10]/(001) - A</li> <li>MULTEM0113: 1x[1-10]/(111) - A</li> <li>MULTEM0114: 2x[1-10]/(001) - A</li> <li>MULTEM0115: 2x[1-10]/(111) - A</li> <li>MULTEM0116: 3x[1-10]/(001) - A</li> <li>MULTEM0117: 3x[1-10]/(111) - A</li> <li>MULTEM0118: 5x[1-10]/(001) - A</li> <li>MULTEM0119: 5x[1-10]/(111) - A</li> <li>MULTEM0120: 10x[1-10]/(001) - A</li> <li>MULTEM0121: 10x[1-10]/(111) - A</li> <li>MULTEM0122: 10x[-1-10]/(001) - B</li> <li>MULTEM0123: 10x[1-10]/(001) - B</li> <li>MULTEM0124: 10x[1-10]/(111) - B</li> <li>MULTEM0125: 10x[-1-10]/(001) - C</li> <li>MULTEM0126: 10x[1-10]/(001) - C</li> <li>MULTEM0127: 10x[1-10]/(111) - C</li> <li>MULTEM0128: 10x[-1-10]/(001) - D</li> <li>MULTEM0129: 10x[1-10]/(001) - D</li> <li>MULTEM0130: 10x[1-10]/(111) - D</li> </ul> <p>In this list, the trailing letter A, B, C, or D indicates whether the shearing is evenly distributed along the precipitate (A), concentrated in a single plane some distance below the specimen surface (B), concentrated in a narrow region some distance below the specimen surface (C), or if the first half of the precipitate is sheared one way while the last half is sheared the opposite way (D). The crystallographic directions in these labels are aligned with [100]||X, [010]||Y and [001]||Z, where X, Y, and Z are the horizontal, vertical, and out-of-plane specimen directions (beam travels along Z).</p> <p>The simulations with the trailing "_s" label correspond to STEM simulations of a smaller and centered region of the precipitate. A final set of STEM simulations with the labels "UCx_*" corresponds to simulations where the shearing configuration is 1x[1-10]/(001) but where the shearing plane lies "x" unit cells below the specimen surface.</p> <p>The models are named differently from the labels, as they are used in a few different simulations and with various parameters as well. Their filenames follow the naming convention of "POSCAR_relaxed_b<N>_i<i>_j<j>.mat", where N, i, and j correspond to the number of shearing events, the burgers vector ID and the shear plane ID, respectively. A Burgers vector IDs 1, 2, and 3 correspond to [-101], [-1-10], and [1-10] directions, respectively, while the shear plane IDs 0 and 1 correspond to the (001) and (111) planes, respectively.</p>
Per-chalcopyrite particle morphological analysis data obtaining from three time-lapse micro-CT images and the PhreeqcRM simulation data
<p>The file "overall_particles.xlsx" includes all the image-based quantifications of all chalcopyrite particles extracted from three time-lapse micro-CT images. These image-based quantifications include specific surface area, volume, liberation ratio, mass before leaching, in the middle of leaching, and after leaching.</p> <p> </p> <p>The file "PhreeqcRM simulation.xlsx" includes the PhreeqcRM simulation results using experimental and image-based data.</p> <p> </p> <p>The file "<a href="https://zenodo.org/api/records/15239075/draft/files/Laplace_Solver-master-main.zip/content" target="_blank" rel="noopener noreferrer">Laplace_Solver-master-main.zip</a>" includes the diffusion simulation source code.</p>
The ST simulated data and High-resolution H&E images used in Tumoroscope
<p>This dataset comprises the simulated ST dataset and H&E (Hematoxylin and Eosin) images corresponding to the spatial transcriptomics samples referenced in the paper titled <i>"</i> <a href="https://doi.org/10.1101/2022.09.22.508914">Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues</a>"</p>
Simulation of a realistic brain phantom for Susceptibility Tensor Imaging
<p>The susceptibility tensor brain phantom is an in-silico brain phantom that serves as ground-truth for new and existing Susceptibility Tensor Imaging algorithms. It contains two different brain phantoms with diffusion (chi_dti.nii.gz) and susceptibility (chi_sti.nii.gz) information each one respectively. Additionally, we upload 2 different local phase simulations for each case:</p> <ul> <li>Angles 1 and Angles 2: Simulations of Gradient Echo acquisitions at two different ranges of rotation angles.</li> <li>phi_6_orientations.nii.gz: Local phase of the 6 different acquisitions</li> <li>phi_12_orientations.nii.gz: Local phase of the 12 different acquisitions</li> <li>angles_6.mat; Angles of the simulations at 6 different acquisitions</li> <li>angles_12.mat: Angles of the simulations at 12 different acquisitions </li> </ul>
Simulated Long-Film image dataset
<p>This dataset contains simulated 10,368 Long-Film images and vertebrae centroid labels from projection of public CT images. The CT images are from SpineWeb [1] and The Cancer Imaging Archive [2]. Details of usage and generation process can be found in [3].</p> <p>[1] B. Glocker, J. Feulner, A. Criminisi, D.R. Haynor, E. Konukoglu, Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans., Med. Image Comput. Comput. Assist. Interv. 15 (2012) 590–8. https://doi.org/10.1007/978-3-642-33454-2_73.<br> [2] K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository., J. Digit. Imaging. 26 (2013) 1045–57. https://doi.org/10.1007/s10278-013-9622-7.<br> [3] Huang Y, Jones CK, Zhang X, Johnston A, Waktola S, Aygun N, Witham TF, Bydon A, Theodore N, Helm PA, Siewerdsen JH, Uneri A. "Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images." Computer Methods and Programs in Biomedicine, 2022 (under review)</p> <p> </p>
Simulated images and profiles
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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)
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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.