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87 results for “DICOM”
DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials
<p>Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. This dataset contains manifests referring to the hematoxylin and eosin (H&E) stained images in Digital Imaging and Communications in Medicine (DICOM) format available from National Cancer Institute Imaging Data Commons (IDC) [1] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov">https://imaging.datacommons.cancer.gov</a>) as of data release v16. The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled on ARST0331, ARST0431, D9602, D9803, and D9902 trials, as described in [2]. Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3], [4] using custom open source scripts and tools available and described here [5]. The resulting converted images were released in IDC in the RMS-Mutation-Prediction collection with the data release v16.</p> <p>To conveniently explore the data available for this dataset, please use this dashboard: <a href="https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9">https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9</a>.</p> <p>Notebooks demonstrating how to use this data are available here: <a href="https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction">https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction</a>.</p> <p>Clinical data accompanying the images is available via SQL interface in IDC BigQuery tables, see details on accessing IDC clinical data in the respective tutorial (<a href="https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb">https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb</a>).</p> <p>The images referred to by the accompanying manifests can be explored and visualized using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/">https://portal.imaging.datacommons.cancer.gov/explore/</a>. Direct link to open the collection is <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction</a>.</p> <p>The GCP and AWS manifests provided with this dataset record can be used to download the corresponding files from the IDC Google Cloud Storage (GCS) or Amazon S3 (AWS) buckets free of charge following the instructions available in IDC documentation here: <a href="https://learn.canceridc.dev/data/downloading-data">https://learn.canceridc.dev/data/downloading-data</a>. Specifically, you will need to install the s5cmd command line tool on your computer (see instructions at <a href="https://github.com/peak/s5cmd#installation">https://github.com/peak/s5cmd#installation</a>), and follow the manifest-specific download instructions accompanying the file list below.</p> <p>If you use the files referenced in the attached manifests, we ask you to please cite this dataset, as well as the publication describing the original dataset [2] and the publication acknowledging IDC [1].</p> <p>Specific files included in the record are:</p> <ol> <li> <p><strong><code>rms_mutation_prediction_gcs.s5cmd</code></strong>: GCS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://storage.googleapis.com run rms_mutation_prediction_gcs.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_aws.s5cmd</code></strong>: AWS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run rms_mutation_prediction_aws.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_dcf.csv</code></strong>: Gen3-based manifest (see details in <a href="https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>).</p> </li> </ol> <p><strong>References</strong></p> <p>[1] A. Fedorov et al., "NCI Imaging Data Commons," Cancer Res., vol. 81, no. 16, pp. 4188–4193, Aug. 2021, doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p> <p>[2] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: <a href="https://dx.doi.org/10.1158/1078-0432.CCR-22-1663">10.1158/1078-0432.CCR-22-1663</a>.</p> <p>[3] National Electrical Manufacturers Association (NEMA), "DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD." Accessed: Aug. 11, 2023. [Online]. Available: <a href="https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8">https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8</a></p> <p>[4] M. D. Herrmann et al., "Implementing the DICOM standard for digital pathology," J. Pathol. Inform., vol. 9, no. 1, p. 37, Jan. 2018, doi: <a href="https://dx.doi.org/10.4103/jpi.jpi_42_18">10.4103/jpi.jpi_42_18</a>. </p> <p>[5] D. Clunie, A. Fedorov, and M. D. Herrmann, ImagingDataCommons/idc-wsi-conversion: Initial release. Zenodo, 2023. doi: <a href="https://dx.doi.org/10.5281/zenodo.8240154">10.5281/zenodo.8240154</a>. </p>
Rt-Cloud Sample Project 'AmygActivation' DICOM Data
<p>This upload contains the same data as published in <a href="https://doi.org/10.5281/zenodo.3677090">our previous zenodo dataset upload</a>. Unlike our previous upload, this version contains data after transferring the DICOMs directly from the Siemens Skyra 3T to our Linux machine (as done in real-time experiments). The purpose of this separate upload is to serve as sample data for our <a href="https://github.com/brainiak/rt-cloud">real-time cloud software</a>, for a <a href="https://github.com/amennen/amygActivation">specific sample project</a>. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
DICOM WG-26 Whole Slide Imaging Annotations Connectathon: Imaging Data Commons entry
<p>This data descriptor contains DICOM Slide Microscopy (SM modality) images and DICOM 2D point and polygon Bulk Annotations (ANN modality) submitted by the Imaging Data Commons team as part of the participation in the 2024 DICOM Working Group 26 Annotations Connectathon (<a href="https://dicom-wg26-connectathons.github.io/2024-annotations/" target="_blank" rel="noopener">https://dicom-wg26-connectathons.github.io/2024-annotations/</a>).</p> <p>Detailed content of the descriptor is as follows:</p> <ol> <li>Images correspond to 3 series selected from the DICOM-converted slides incuded in the TCGA-READ collection (originally shared in vendor-specific format in <a href="https://portal.gdc.cancer.gov/projects/TCGA-READ" target="_blank" rel="noopener">https://portal.gdc.cancer.gov/projects/TCGA-READ</a>) and available from NCI Imaging Data Commons [1] at <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_read" target="_blank" rel="noopener">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_read</a>. Specifically, the following series are included, as defined by their `PatientID`, `StudyInstanceUID` and `SeriesInstanceUID` values. Each individual series contains multiple files corresponding to different resolution layers, shared as zip files. <ol> <li>`TCGA-AF-2687.zip`: series `1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.213661963103110408605329613498871186883/series/1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.213661963103110408605329613498871186883/series/1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0</a></li> <li>`TCGA-AF-2689.zip`: series `1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.312916405820155829215771528638931942827/series/1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.312916405820155829215771528638931942827/series/1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0</a></li> <li>`TCGA-AF-2690.zip`: series `1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.158926540358526295486644564130790202309/series/1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.158926540358526295486644564130790202309/series/1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0</a></li> </ol> </li> <li>`polygon_annotations.zip`: 2D polygon annotations of the boundary of the cell nuclei. Conversion into DICOM ANN was performed from the original content shared in [2].</li> <li>`point_annotations.zip`: 2D point annotations corresponding to the centroids of the cell nuclei defined in the prior polygon annotations.</li> <li>`rectangle_annotations.zip`: 2D rectangle annotations corresponding to the axis-aligned bounding box of the cell nuclei defined in the prior polygon annotations.</li> <li>`ellipse_annotations.zip`: 2D ellipse annotations corresponding to the ellipse fit to the cell nuclei defined in the prior polygon annotations.</li> <li>`screenshots.pdf`: screenshots demonstrating visualization of the included annotations in the open source Slim viewer (https://github.com/ImagingDataCommons/slim) (note these visualizations were produced using a development branch of the software)</li> <li>`run_verify.sh`: script that was used to run `dciodvfy` validator and save validation results.</li> </ol> <p>Zip files with the annotations also include the output of the `dciodvfy` validator (https://dclunie.com/dicom3tools/dciodvfy.html) version `20240227102104` for each of the files.</p> <p> </p>
Open-Access DICOM MRI session
<p>This is an open-access DICOM MRI session of a healthy subject (Colin Vanden Bulcke) containing several MRI sequences:</p> <ul> <li>MPRAGE T1-weighted</li> <li>MP2RAGE</li> <li>FLAIR</li> <li>3D EPI</li> </ul>
DICOM images of abdomen CT phantom in axial, helicoid, iterative and dual energy scans.
<p>The two ZIP files contain the images of a CT phantom of abdomen. DualEnergy.zip contains files of acquisition at 80 kVp and 140 kVp. The second file HelicalAxialIterative.zip contains acquisition done in axial, helicoidal (aka spiral) and iterative reconstruction (Saphire) modes. Axial images have AbdSeq in the name, Helicoidal have Abdomen and B in the filter name, Iterative have Abdomen and I in the filter name. You can compare Exposure in mAs for each acquisition modality, how it diminishes from sequential/helicoidal/axial through helicoidal/spiral to iterative. This scanner if programmed with iterative acquisition can still reduce exposure from helicoidal scan. The acquisition was done on Siemens-Healthineers Somatom Confidence CT scanner. </p> <p>Antonio Ortiz Lora antonio.ortiz.lora.sspa@juntadeandalucia.es</p> <p>Marcin Balcerzyk mbalcerzyk@us.es</p> <div class="ms-editor-squiggler"> </div>
An MRI DICOM data set of the head of a normal male human aged 52
<p>This zip file contains a DICOM data set of magnetic resonance images a normal male mathematics professor aged 52. The experimental subject is the author. The MRI scans are T2 weighted turbo-spin-echo (T2W TSE) and T1 weighted Fast Field Echo (T1W FFE).</p> <p>The subject suffers from a small vertical strabismus (hypertropia), a misalignment of the eyes, which is visible in this data set.</p> <p>The author would like to thank the Radiology Department at the Macclesfield General Hospital for performing the scan.</p> <p> </p>
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - DICOM Database
<p>This dataset contains DICOM versions of the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.</p> <p>NRRD versions can be found in http://dx.doi.org/10.5281/zenodo.20766 (doi:10.5281/zenodo.20766).</p>
DICOM converted annotations for the Prostate-MRI-US-Biopsy collection
<p>This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (<a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy</a>). Prostate-MRI-US-Biopsy 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 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations.</p><p>This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed 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>Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (<a href="https://www.pixelmed.com/dicomtoolkit.html">https://www.pixelmed.com/dicomtoolkit.html</a>). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (<a href="https://www.slicer.org/">https://www.slicer.org/</a>) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (<a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a>) [5]. 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 as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: <a href="https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion">https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion</a>.</p><p>Specific files included in the record are:</p><ol><li><strong>Prostate-MRI-US-Biopsy-DICOM-Annotations.zip</strong>: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm</li><li><strong>referenced_images_sorted-idc_file_manifest.s5cmd</strong>: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (<a href="https://github.com/peak/s5cmd">https://github.com/peak/s5cmd</a>), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download.</li></ol><p><strong>References</strong></p><p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. <i>Cancer Res.</i> <strong>81,</strong> 4188–4193 (2021). doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p><p>[2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">10.7937/TCIA.2020.A61IOC1A</a></p><p>[3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: <a href="https://doi.org/10.1016/j.juro.2012.08.095">10.1016/j.juro.2012.08.095</a> </p><p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: <a href="https://doi.org/10.1007/s10278-013-9622-7">10.1007/s10278-013-9622-7</a></p><p>[5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. <i>Cancer Res. </i><strong>77,</strong> e87–e90 (2017). DOI: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-17-0336">10.1158/0008-5472.CAN-17-0336</a>.</p>
Pan-Cancer-Nuclei-Seg-DICOM: DICOM converted Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pan-Cancer-Nuclei-Seg-DICOM" target="_blank" rel="noopener">Pan-Cancer-Nuclei-Seg-DICOM</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div>This collection contains automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types earlier published in [2] (<a href="https://doi.org/10.7937/TCIA.2019.4A4DKP9U">https://doi.org/10.7937/TCIA.2019.4A4DKP9U</a>) stored in DICOM Bulk Annotation and DICOM Segmentation formats.</div> <div> </div> <div>DICOM Bulk Annotation nuclei annotations are stored as closed polygons along with the area of each nuclei. DICOM Segmentation version contains binary segmentations obtained by rasterizing the polygon contours. </div> <div> </div> <div>The annotations correspond to digital pathology images from the TCGA-BLCA,TCGA-BRCA,TCGA-CESC,TCGA-COAD,TCGA-GBM,TCGA-LUAD,TCGA-LUSC,TCGA-PAAD,TCGA-PRAD,TCGA-READ,TCGA-SKCM,TCGA-STAD,TCGA-UCEC,TCGA-UVM collections available in NCI Imaging Data Commons.</div> <div> </div> <div>To learn how these files are organized and how to access the content programmatically, see this documentation page: <a href="https://highdicom.readthedocs.io/en/latest/ann.html">https://highdicom.readthedocs.io/en/latest/ann.html</a>.</div> <div> </div> <div>Conversion of the nuclei segmentations from the original format into DICOM ANN and SEG representations was done using the code available in <a href="https://doi.org/10.5281/zenodo.13871765">10.5281/zenodo.10632181</a>.</div> <div> </div> <div>Annotations corresponding to this container ID in the source failed to convert due to the pixel matrix being too large to store: <code>TCGA-OL-A66K-01Z-00-DX1</code></div> <div> </div> <div>The following container IDs from the source annotations have failed due to inability to find the annotated images using the container IDs:</div> <div> <pre><code>TCGA-CU-A3QU-01Z-00-DX1 TCGA-A2-A0D1-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-BH-A0B2-01Z-00-DX1 TCGA-E2-A15E-01Z-00-DX1 TCGA-E2-A1IP-01Z-00-DX1 TCGA-F4-6857-01Z-00-DX1 TCGA-12-0773-01Z-00-DX4 TCGA-35-3621-01Z-00-DX1 TCGA-49-4486-01Z-00-DX1 TCGA-33-4587-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX2 TCGA-D9-A4Z6-01Z-00-DX1 TCGA-EE-A17Y-01Z-00-DX1 TCGA-EE-A29R-01Z-00-DX1 TCGA-EE-A2A0-01Z-00-DX1 TCGA-EE-A2MS-01Z-00-DX1 TCGA-ER-A199-01Z-00-DX1 TCGA-ER-A1A1-01Z-00-DX1 TCGA-ER-A2NC-01Z-00-DX1 TCGA-FS-A1Z7-06Z-00-DX10 TCGA-FS-A1Z7-06Z-00-DX11 TCGA-FS-A1Z7-06Z-00-DX12 TCGA-FS-A1Z7-06Z-00-DX13 TCGA-FS-A1ZN-01Z-00-DX10 TCGA-FS-A1ZN-01Z-00-DX11 TCGA-FS-A1ZW-06Z-00-DX10 TCGA-FS-A1ZW-06Z-00-DX11 TCGA-GN-A261-01Z-00-DX1 TCGA-GN-A266-01Z-00-DX1 TCGA-GN-A268-01Z-00-DX1 TCGA-GN-A26A-01Z-00-DX1 TCGA-XV-AB01-01Z-00-DX1 TCGA-AJ-A23O-01Z-00-DX1 TCGA-AP-A056-01Z-00-DX1 TCGA-BK-A139-01Z-00-DX1 TCGA-E6-A1M0-01Z-00-DX1</code></pre> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 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: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> </div> </div> <div>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</div> <div> </div> <div>[2] Hou, L., Gupta, R., Van Arnam, J. S., Zhang, Y., Sivalenka, K., Samaras, D., Kurc, T., & Saltz, J. H. (2019). Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.4A4DKP9U</div>
UKRIN-MAPS MRI DICOM Parameters
<p>Excel file containing lists of MRI parameters and the expected values for different manufacturers (GE, Philips, Siemens).</p> <p>The purpose is to perform QC checks that validate Kidney MRI protocols.</p>
Pancreas patients dicom
<p>This dataset contains the dicom data for 6 pancreas patients. Images and annotations are included. For details, please refer to https://github.com/xuqifan897/EndtoEnd</p>
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>
DICOM converted Slide Microscopy images for the CPTAC-LSCC collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=cptac_lscc">CPTAC-LSCC</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> <span>This collection contains subjects from the National Cancer Institute’s <u><a href="https://proteomics.cancer.gov/programs/cptac" rel="nofollow">Clinical Proteomic Tumor Analysis Consortium</a></u> Lung Squamous Cell Carcinoma (CPTAC-LSCC) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC Phase 3 patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.</span></p> <p> </p> <p> <span>Please see the <a href="">CPTAC-LSCC <i></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.</span></p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>cptac_lscc-idc_v3-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>cptac_lscc-idc_v3-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>cptac_lscc-idc_v3-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation (DICOM VERSION)
<p>We constructed a groundbreaking lung CT dataset, which includes 330 annotated nodules from 95 patients. It is worth noting that we have integrated the results of patient clinical diagnosis, frozen diagnosis, and pathological diagnosis, supplementing this with labeled lung cancer types on 308 samples containing nodules.</p>
DICOM converted Slide Microscopy images for the TCGA-UVM collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_uvm">TCGA-UVM</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> Uveal (intraocular or eye) melanoma develops in the pigment cells of the uvea, which is the middle layer of the eye. The uvea consists of three main parts: the iris, ciliary body, and choroid. Compared to tumors of the iris, tumors of the ciliary body and choroid tend to be larger and more likely to spread to other parts of the body. TCGA studied tumors from all three parts of the uvea.</p> <p> Please see the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers/melanoma-eye" target="_blank">TCGA-UVM</a> information page to learn more about the images and to obtain any supporting metadata for this collection.</p> <p> Citation guidelines can be found on the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing-tcga" target="_blank">Citing TCGA in Publications and Presentations</a> information page.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_uvm-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_uvm-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_uvm-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-OV collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_ov">TCGA-OV</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) data collection is part of a larger effort to enhance the TCGA http://cancergenome.nih.gov/ data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions are working to archive a large portion of the radiological images of the genetically-analyzed OV cases.</p> <br> <p> Please see the <a href="">TCGA-OV <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_ov-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_ov-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_ov-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-LGG collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_lgg">TCGA-LGG</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <div> <strong>Note: This collection has special restrictions on its usage. See <a href="">Data Usage Policies and Restrictions <i></i></a>.</strong></div> <br><p>The <a href="http://imaging.cancer.gov/" target="_blank"><u>Cancer Imaging Program (CIP)</u></a> is working directly with primary investigators from institutes participating in TCGA to obtain and load images relating to the genomic, clinical, and pathological data being stored within the <a href="http://tcga-data.nci.nih.gov/" target="_blank">TCGA Data Portal</a>. Currently this large MR multi-sequence image collection of low grade glioma patients can be matched by each unique case identifier with the extensive gene and expression data of the same case from The Cancer Genome Atlas Data Portal to research the link between clinical phenome and tissue genome.</p><br> <p>Please see the <a href="">TCGA-LGG <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_lgg-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_lgg-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_lgg-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-KICH collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_kich">TCGA-KICH</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> The Cancer Genome Atlas-Kidney Chromophobe (TCGA-KICH) data collection is part of a larger effort to enhance the The Cancer Genome Atlas (TCGA) <a href="https://cancergenome.nih.gov/">https://cancergenome.nih.gov/</a> data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions has archived a large portion of the radiological images of the genetically-analyzed KICH cases.</p> <p> Please see the <a href="">TCGA-KICH <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_kich-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_kich-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_kich-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-ACC collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_acc">TCGA-ACC</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> Adrenocortical carcinoma is a rare cancer that develops in the outer layer of tissue of the adrenal glands, organs that lie on top of each kidney. This outer layer known as the adrenal cortex produces important hormones called steroids that help the body deal with stress, regulate blood pressure and the amount of salt in the blood, as well as cause the body to acquire masculine or feminine characteristics. A tumor of the adrenal cortex can produce either no hormones or excess hormones.</p> <p> Please see the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers/adrenocortical" target="_blank">TCGA-ACC</a> information page to learn more about the images and to obtain any supporting metadata for this collection.</p> <p>Citation guidelines can be found on the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing-tcga" target="_blank">Citing TCGA in Publications and Presentations</a> information page.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_acc-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_acc-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_acc-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-PCPG collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_pcpg">TCGA-PCPG</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> Paraganglioma is a rare cancer that originates in the nerve cells of the adrenal glands, organs on top of each kidney that produce important hormones. Paraganglioma that develops in the center of the adrenal gland is called pheochromocytoma. Paraganglioma that forms outside of the adrenal gland, often along blood vessels and nerves in the head and neck, is called extra-adrenal paraganglioma, or simply paraganglioma. Each year, between 2 and 8 people per million worldwide are diagnosed with paraganglioma and pheochromocytoma. 10% of all cases occur in children. In both adults and children, pheochromocytoma is more common than paraganglioma. No known environmental, dietary, or lifestyle risk factors have been associated with these cancers.</p> <p> Please see the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers/paraganglioma" target="_blank">TCGA-PCPG</a> information page to learn more about the images and to obtain any supporting metadata for this collection.</p> <p> Citation guidelines can be found on the <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing-tcga" target="_blank">Citing TCGA in Publications and Presentations</a> information page.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_pcpg-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_pcpg-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_pcpg-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</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 files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">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: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
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.