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438 results for “3D imaging”
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
3D image of an assembly of rice grains
<p>3D reconstruction of an assembly of rice grains. The image was acquired on the collaborative microtomography platform at Laboratoire Navier (http://navier.enpc.fr).</p> <p>Ordinary, long-grain rice is poured in a plastic container, 50mm in diameter. The average length of the grains is about 6.5 mm.</p> <p>The X-ray source is a Hamamatsu L10801 X-ray source (maximum voltage: 230V, maximum current: 1mA). Combined with the the Paxscan Varian 2520V flat-panel X-ray imager (1536x1920 pixels, pixel pitch 127µm), this setup leads to a voxel size of approx. 0.030mm. The specimen was scanned at 100kV and 300µA with an imager frame-rate of 6 images per second. To reduce noise, 12 radiographs were averaged to produce one projection; the total number of projections was 1440.</p> <p>3D reconstruction was carried out using standard tools developed by RX Solutions France. Contrast, resolution and signal-to-noise ratio were all excellent, so that the most basic reconstruction procedure resulted in very high quality 3D images.</p> <p>The dataset is provided as a set of 689 TIFF images, 1747×1751 pixels. It is used as illustrative material for a series of posts called “Orientation correlations among rice grains” on my blog (see related identifiers below).</p> <p> </p>
FIGURE 8 3D in Automated segmentation of insect anatomy from micro-CT images using deep learning
FIGURE 8 3D volume of ant brain reconstructed from 2D images (original 520 × 520 px) predicted by the algorithm. 3D reconstructed brain prediction of an Atta texana worker.
Images supporting: Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation
<p>Two image datasets (as zip files) including all images analyzed in the manuscript Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. Images are of pancreatic adenocarcinoma (PDAC) cystic spheroid samples grown in either BME or Matrigel. Some images have background noise in the form of iron oxide nanoparticles introduced to them.</p>
TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images
<p>We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.</p> <p>Note that there are two files here:</p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a></p> <p>and</p> <p><br> <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set.</p> <p>https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z</p> <p><a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content</p> <p>See ReadMe file in side the downloaded files for more details</p> <p>The full dataset (>100gb) can be found here:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a></p> <p>https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a></p> <p>See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details</p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a></strong></p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a></strong></p> <p> </p> <p><strong>************************************************************************************</strong></p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a></p> <p>The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.</p> <p> </p> <p> </p> <p> </p>
PTI datasets: 3D imaging
<p>This dataset includes the following data reported in the PTI paper (<a href="https://www.biorxiv.org/content/10.1101/2020.12.15.422951v2">link</a>). These datasets can be read and processed using the provided notebooks (<a href="https://github.com/mehta-lab/waveorder/tree/master/examples/uPTI_experiment">link</a>) with the waveorder package (<a href="https://github.com/mehta-lab/waveorder">link</a>). The zarr arrays (live one level below Col_x in the zarr files) in these datasets can also be visualized with the python image viewer (<a href="https://napari.org/">napari</a>). You will need the ome-zarr plugin in napari and drag the zarr array to the napari viewer. </p> <p>1. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_small.zip?versionId=4e890f01-6442-41eb-bffa-0387cfae510b">Anisotropic_target_small.zip</a> includes two zarr files that save the raw intensity images and processed physical properties of the small anisotropic target (double line-scan, 300-fs pulse duration):</p> <p>- Anisotropic_target_small_raw.zarr: array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 96, 300, 300)</p> <p>- Anisotropic_target_small_processed.zarr: </p> <ul> <li>(Pos0 - Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 300, 300)</li> <li>(Pos1 - Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 300, 300)</li> </ul> <p> </p> <p>2. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_raw.zip?versionId=d783651b-de1b-45fd-8e71-df17073ced40">Anisotropic_target_raw.zip</a> includes the raw intensity images of another anisotropic target (single line-scan, 500-fs pulse duration):</p> <p>- data: 9 x 96 (pattern x z-slices) raw intensity images (TIFF) of the target with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- bg: - data: 9 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p> </p> <p>3. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_processed.zip?versionId=4638a49f-82c2-4156-9c6c-e38742db0408">Anisotropic_target_processed.zip</a> includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the anisotropic target (single line-scan, 500-fs pulse duration):</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 700, 700) (cropping the star target region)</p> <p> </p> <p>4. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Mouse_brain_aco_raw.zip?versionId=2c2a62bf-ab1a-4473-a064-4a58964aca56">Mouse_brain_aco_raw.zip</a> includes the raw intensity images of the mouse brain section at aco region:</p> <p>- data: 9 x 96 (pattern x z-slices) raw intensity images (TIFF) of the mouse brain section with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- bg: - data: 9 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p> </p> <p>5. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Mouse_brain_aco_processed.zip?versionId=22423258-3fe0-4240-b88e-e40f97321447">Mouse_brain_aco_processed.zip</a> includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the mouse brain section at aco region:</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 1024, 1224)</p> <p> </p> <p>6. Cardiomyocytes_(condition)_raw.zip includes two zarr files that save the raw PTI intensity images and the deconvolved fluorescence images of the cardiomyocytes with the specified (condition):</p> <p>- Cardiomyocytes_(condition)_raw.zarr:</p> <ul> <li>(Pos0) raw intensity images with the array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 32, 1024, 1224)</li> <li>(Pos1) background intensity images with the array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 1, 1024, 1224)</li> </ul> <p>- Cardiomyocytes_(condition)_fluor_decon.zarr: deconvolved fluorescence images with the array size in the format of (T, C, Z, Y, X) = (1, 3, 32, 1024, 1224)</p> <p> </p> <p>7. Cardiomyocytes_(condition)_processed.zip includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the cardiomyocytes with the specified (condition):</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 32, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 32, 1024, 1224)</p> <p> </p> <p>8. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/cardiac_tissue_H_and_E_processed.zip?versionId=45a5c99c-b2fb-4a23-94f2-1ccc96e2d5a7">cardiac_tissue_H_and_E_processed.zip</a> and <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Human_uterus_section_H_and_E_raw.zip?versionId=83ded89e-b62a-4f06-be0f-f18d28eaf0aa">Human_uterus_section_H_and_E_raw.zip</a> include the raw PTI intensity and H&E images of the cardiac tissue and human uterus section:</p> <p>- data: 10 x 40 (pattern x z-slices) raw intensity images (TIFF) of the target with size of (2048, 2448) -> 4 channels of (1024, 1224), the last channel is for images acquired with LCD turned off (the light leakage needed to be subtracted from the data)</p> <p>- bg: - data: 10 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p>- fluor: 3 x 40 (RGB x z-slices) raw H&E intensity images (TIFF) of the sample with size of (2048, 2448)</p> <p>- fluor_bg: 3 (RGB) raw H&E intensity images (TIFF) of the background with size of (2048, 2448)</p> <p> </p> <p>9. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/cardiac_tissue_H_and_E_processed.zip?versionId=45a5c99c-b2fb-4a23-94f2-1ccc96e2d5a7">cardiac_tissue_H_and_E_processed.zip</a> and <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Human_uterus_section_H_and_E_processed.zip?versionId=5080de3f-8527-40e6-bc80-6a6ec4f9bcf8">Human_uterus_section_H_and_E_processed.zip</a> include three zarr files that save the processed scattering potential tensor components, the processed physical properties, and the white-balanced H&E intensities of the cardiac tissue and human uterus section:</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 40, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 40, 1024, 1224)</p> <p>- H_and_E.zarr: (H_and_E) array size in the format of (T, C, Z, Y, X) = (1, 3, 40, 1024, 1224)</p>
Multiphoton imaging of melanoma 3D models with plasmonic nanocapsules
<p>Dataset of https://www.sciencedirect.com/science/article/pii/S1742706122000617?via%3Dihub#fig0001</p>
Supplementary Material of : Large-Scale 3D Image Segmentation Using Scattering Networks
<p>The reader will find here the supplementary material associated with the manuscript "Large-Scale 3D Image Segmentation Using<br> Scattering Networks" submitted to IEEE Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2022.</p>
Codes for "High-throughput parallel optofluidic 3D-imaging flow cytometry"
<p>Codes used in Ugawa & Ota. "High-throughput parallel optofluidic 3D-imaging flow cytometry". Small size data is also included.</p>
Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'
<p>Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics<br> on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation.<br> Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.</p>
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
<p>Dataset related to the publication: "Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research"</p>
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
3D reconstruction of an NIH/3T3 mouse fibroblast cell imaged by the SXT-100.
<p>This dataset (*.mrc file) is a reconstructed 3D volume obtained by soft X-ray tomography on NIH/3T3 cells. The cells were grown on a 200 mesh 3.05 mm EM finder grid with a Quantifoil Holey Carbon support. The sample was vitrified by plunge-freezing in liquid ethane. The tomogram was collected with a pixel size of 28.85 nm over the tilt range from -53 to 53.5 degrees with 1.5 deg step size and 116 s exposure per tilt.</p>
Correlative microscopy of rat cultured hippocampal pyramidal cell from 40x confocal imaging to super-resolution 93x 3D STED of dendritic spines
<p>This dataset contain multi-scale image of rat hippocampal pyramidal cell related to our paper "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p>
Figure 4 in New directions in weed management and research using 3D imaging
Figure 4. Three-dimensional point cloud reconstructions of soybean (A, top view; B, front view) and cereal rye (Secale cereale L.) (C, top view; D, front view). Note the voids in the soybean point cloud (B) caused by dense canopy cover. Such voids are largely absent in cereal rye (D) due to a more even canopy with greater light penetration.
Figure 3 in New directions in weed management and research using 3D imaging
Figure 3. Data pipeline for calculating canopy height and estimating biomass in the field using red, green, and blue (RGB) images and depth data.
Figure 2 in New directions in weed management and research using 3D imaging
Figure 2. Red,green,and blue (RGB) image of soybeans and weeds (A) and corresponding 3D point cloud reconstruction (B). Lower panels show point cloud reconstructions from different angles,including a top view (C), top view offset 45° from vertical (D), front view (E), under canopy and offset 45° (F), directly under canopy (G), facing canopy from behind (H), facing canopy offset 45° right (I), side view (J), and facing canopy offset 45° left (K).
Figure 1 in New directions in weed management and research using 3D imaging
Figure 1. Use of images taken from different angles to create a 3D reconstruction in structure-from-motion (SfM; top) vs. stereo-vision photogrammetry (bottom).
ScienceDex guides
Understand access before you commit
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.