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S-LDSC reference annotations for CT-FM model (Kim et al., 2024 in prep.)
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Confocal and micro-CT data of Eucinetus spp. from Kachin amber
<div>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotypes of <em>Eucinetus debilispinus</em> (NIGP203939), <em>Eucinetus panghongae</em> (NIGP203940) and <em>Eucinetus zhenhuai</em> (NIGP203941). This dataset is associated with the publication "Three new species of the extant genus <em>Eucinetus</em> from mid-Cretaceous amber of northern Myanmar (Coleoptera: Eucinetidae)" (DOI:10.11646/zootaxa.5492.2.4).</div> <div> </div> <div>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</div> <div> </div> <div>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: NIGP203939 [isotropic voxel size, 2.2345 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 2 s; projections, 1401]; NIGP203940 [isotropic voxel size, 2.9369 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 2601]; NIGP203941 [isotropic voxel size, 3.5820 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 2 s; projections, 2501]. The TIFF stacks are provided.</div>
Pancreas-CT-SEG: DICOM-converted manual segmentations of pancreas for the Pancreas-CT collection
<p>This dataset contributes DICOM-converted annotations of the pancreas to the publicly available National Cancer Institute Imaging Data Commons [1] Pancreas-CT collection (<a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=pancreas_ct">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=pancreas_ct</a>). The Pancreas-CT [2] collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 80 manual segmentations of the pancreas in the NIFTI format. Converting these NIFTI manual annotations to DICOM allows users to visualize the CT images and corresponding pancreas annotations in public DICOM-based viewers. The DICOM segmentation format includes additional metadata describing the contained segmented structure using standard SNOMED-CT terminology, and allowing users to easily map them to the reference DICOM CT images. The converted DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.</p> <p>To visualize and explore segmentations in this collection use this link to open it in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pancreas-CT-SEG" target="_blank" rel="noopener">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pancreas-CT-SEG</a>.</p> <p>Conversion from NIFTI to DICOM SEG modality was performed using the dcmqi toolkit (https://github.com/QIICR/dcmqi)[4]. Resulting objects were validated using dicom3tools dciodvfy (<a href="https://www.dclunie.com/dicom3tools.html">https://www.dclunie.com/dicom3tools.html</a>). Details describing the conversion process are provided in this GitHub repository: <a href="https://github.com/ImagingDataCommons/idc-dicom-seg-conversions" target="_blank" rel="noopener">https://github.com/ImagingDataCommons/idc-dicom-seg-conversions</a>.</p>
Tension clamp X-ray CT data of non-crimp fabric based fibre composite under fatigue loading
<p>Tension clamp X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</p>
CT image volume, gray values and pore structure parameters of hydrate formation in sands
<p>The dataset consists of CT raw volume and gray values of hydrate formation (hydrate saturation 0, 14%, 35% and 51%), methane hydrate growth pattern, and the pore structure parameters. The dataset associated with the manuscript entitled " Investigation on the multi-parameter of hydrate-bearing sands using nano-focus X-ray computed tomography" .</p> <p> </p>
Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation
<p>Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation. Medical Physics, 2018 Nov;45(11):5343-5351. doi: 10.1002/mp.13174.</p>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42
<p>This upload contains samples 38 - 42 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37
<p>This upload contains samples 33 - 37 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32
<p>This upload contains samples 25 - 32 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24
<p>This upload contains samples 17 - 24 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16
<p>This upload contains samples 9 - 16 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8
<p>This upload contains samples 1 - 8 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
CT Dataset of a Human Head
<p>Cranial CT scan of a 51 years old male obtained in a follow-up check after drill hole trepanation frontal left. The data originate from a GE Healthcare CT System Lightspeed VCT with the following study parameters: Slice thickness 0.625 mm, Reconstruction diameter 252 mm and Inplane pixel spacing 0.492 x 0.492 mm</p>
CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data
<p>This is the train and testing dataset of Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. This data consists of images of Abdominal CT and MRI from different patients.</p> <p>There are 20 training and 20 testing cases in the CT dataset. MRI dataset contains 20 training and 20 testing cases with T1-Dual and T2 SPIR sequences. Train data contains both DICOM images and their ground truth masks. The testing set only contains DICOM images. In CT cases only livers were annotated. In MRI cases, livers, left/right kidneys, and spleens were annotated. For further information about the data and challenge, please visit <a href="https://chaos.grand-challenge.org/">https://chaos.grand-challenge.org/</a> and read the <a href="https://www.dropbox.com/s/b8ka7tcxm45mlq4/CHAOS_Submission_Manual_new.pdf?dl=1">CHAOS_Submission_Manual.pdf</a></p> <p><em><strong>Important note: </strong>Ground truths/references of the test data are reserved for challenge validation and <strong>will never be shared publicly. </strong>Such requests will be ignored<strong>.</strong></em></p> <p>Scientists may use this data not only join to the CHAOS challenge but also for other works as long as they give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>Bibtex:</p> <pre><code>@dataset{CHAOSdata2019, author = {Ali Emre Kavur and M. Alper Selver and Oğuz Dicle and Mustafa Barış and N. Sinem Gezer}, title = {{CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data}}, month = Apr, year = 2019, publisher = {Zenodo}, version = {v1.03}, doi = {10.5281/zenodo.3362844}, url = {https://doi.org/10.5281/zenodo.3362844} }</code></pre> <p>IEEE Style:</p> <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, and N. Sinem Gezer, "CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data". Zenodo, 11-Apr-2019.</code></pre> <p>APA Style:</p> <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, & N. Sinem Gezer. (2019). CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3362844</code></pre> <p> </p>
TCIA full and low dose CT images and volumetric normal pancreas segmentations
<p>This publicly available CT dataset was downloaded from 50 portal venous phase abdominal CT scans (3mm-slice thickness) performed in 50 patients with liver metastases— from The Cancer Imaging Archive (TCIA).These CT scans were acquired with routine radiation dose levels per standard clinical protocols on SOMATOM Definition Flash CT scanner (Siemens Healthcare, Forchheim, Germany).Each study had been postprocessed to include a second reconstructed CT dataset at a simulated 25% radiation dose level. The mean (standard deviation [SD]) radiation dose for the full dose and the reduced dose CT datasets were 14.7 mSv (8 mSv) and 3.7 mSv (2 mSv), respectively. After a curation process by radiologists, 9 of 50 cases were excluded [chronic calcific pancreatitis (n = 1), postsurgical changes in pancreas (n = 2) and pancreatic lesions (n = 6)]. Volumetric pancreas segmentations were done on all the remaining 41 cases [16 males and 25 females, mean (SD) patient age: 60.8 years (14.7 yrs)] independently by the two radiologists (R1 and R2). Segmentations were first done on the full dose CT dataset and then repeated on the reduced dose CT dataset by both R1 and R2 after a gap of at least 24 hours to allow for memory extinction.</p>
TotalSegmentator-CT-Segmentations: TotalSegmentator segmentations and radiomics features for NCI Imaging Data Commons CT images
<p>This dataset contributes volumetric segmentations of the anatomic regions in a subset of CT images available from NCI Imaging Data Commons [1] (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) automatically generated using the TotalSegmentation model v1.5.6 [2]. The initial release includes segmentations for the majority of the CT scans included in the National Lung Screening Trial (NLST) collection [3], [4] already available in IDC. Direct link to open this analysis result dataset in IDC (available after release of IDC v18): <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TotalSegmentator-CT-Segmentations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TotalSegmentator-CT-Segmentations</a>. </p> <p>Specifically, for each of the CT series analyzed, we include segmentations as generated by TotalSegmentator, converted into DICOM Segmentation object format using dcmqi v1.3.0 [5], and first order and shape features for each of the segmented regions, as produced by pyradiomics v3.0.1 [6]. Radiomics features were converted to DICOM Structured Reporting documents following template TID1500 using dcmqi. TotalSegmentator analysis on the NLST cohort was executed using Terra platform [7]. Implementation of the workflow that was used for performing the analysis is available at <a href="https://github.com/ImagingDataCommons/CloudSegmentator">https://github.com/ImagingDataCommons/CloudSegmentator</a> [8].</p> <p>Due to the large size of the files, they are stored in the cloud buckets maintained by IDC, and the attached files are the manifests that can be used to download the actual files.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset and the preprint describing how it was generated [9].</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the TotalSegmentator segmentations (in DICOM SEG format) and pyradiomics measurements (in DICOM SR format) files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file. E.g., <code>idc download totalsegmentator_ct_segmentations_aws.s5cmd</code></li> </ol> <p>Other files included in the record are:</p> <ol> <li>firstorder and shape radiomics features extracted using pyradiomics, and organized one file per segmented structure (see README file in the zip file for details on how those are organized) <ol> <li>pyradiomics_features_csv.zip: saved in CSV format</li> <li>pyradiomics_features_parquet.zip: saved in Parquet format</li> </ol> </li> </ol> <h3>Support</h3> <p>If you have any questions about this dataset, or if you experience any issues, please reach out to Imaging Data Commons support via <a href="mailto:support@canceridc.dev">support@canceridc.dev</a> or (preferred) IDC Forum at <a href="https://discourse.canceridc.dev" target="_blank" rel="noopener">https://discourse.canceridc.dev</a>.</p>
Confocal and micro-CT data of Domipteron gaoi, holotype, NIGP205048
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Domipteron gaoi</em> (NIGP205048) from Miocene Dominican amber. This dataset is associated with the publication "<em>Domipteron</em>, a new genus of Calopterini from Miocene Dominican amber (Coleoptera: Elateroidea: Lycidae)" (DOI:10.1017/jpa.2024.72).<br> <br>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.<br> <br>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 10.412 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 3.5 s; projections, 2001. The TIFF stack is provided.</p>
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
S-LDSC CANCER annotations for CT-FM model (Kim et al., 2024 in prep.)
Open the record for dataset details and reuse information.
Dataset of High-Resolution Micro-CT Imaging of Tumor Invasion and Metastasis in a Murine Esophageal Cancer PDX Model
<p>This dataset features high-resolution micro-CT imaging data capturing the progression of tumor invasion and metastasis in an orthotopic patient-derived xenograft (PDX) model of esophageal cancer. Using contrast-enhanced micro-CT, we visualized detailed patterns of tumor invasion, including budding, multicellular streaming, and expansive growth, across multiple abdominal organs such as the stomach, pancreas, liver, and spleen. The dataset includes two specimens, highlighting both the primary tumor site and extensive metastases throughout the abdominal cavity. Our imaging preserved the native tissue architecture, providing a unique three-dimensional view of tumor-host interactions. This collection offers valuable insights for researchers studying the dynamics of esophageal cancer invasion and metastasis. Detailed descriptions of the micro-CT scanning parameters, image analysis, and sample preparation are provided within the dataset archive.</p>
ScienceDex guides
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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)
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DANDI Archive for NWB datasets
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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.