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87 results for “DICOM”
DICOM converted Slide Microscopy images for the HTAN-HMS 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=htan_hms">HTAN-HMS</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> <p>The Human Tumor Atlas Network (HTAN) [2], part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. </p> <p>We are constructing a multi-dimensional atlas of pre-melanoma focused on understanding genetic and epigenetic events that transform melanocytes into invasive tumors. Melanoma is a cancer of increasing prevalence that is curable with minor surgery if detected early but life-threatening when it metastasizes. Melanomas metastasize when still small, making early detection essential but challenging. Our atlas will delineate the precise sequence of events leading up to pre-melanoma through detailed spatial analysis of cell-autonomous events such as oncogene mutation and non-autonomous events such as escape from immune surveillance. The atlas is based on highly-multiplexed tissue imaging and single cell sequencing and focused on samples in which the full sequence of events from atypia to invasive melanoma can be visualized in a single specimen. The atlas will serve as a publicly accessible resource for research scientists, physicians, and patients and improve our ability to (i) highlight lesions likely to progress to cancer, (ii) identify high-risk patients to inform decisions on surgery, (iii) identify low-risk patients to reduce unnecessary procedures, (iv) design improved procedures for routine screening of all individuals, and (v) inform treatment options when surgery is insufficient. Complementary studies with similar goals (but not supported by HTAN) are studying later stage melanomas.</p> <p>Please see the <a href="https://humantumoratlas.org/hta7/"> HTAN-HMS <em> </em> </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://docs.google.com/document/d/1seSp9yo8mqMK5sFXd4LEf_t_4SEofZi-1p-M75R1lhk/edit#heading=h.ybiv33wzts40"> HTAN Publication Policy <em> </em> </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>htan_hms-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>htan_hms-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>htan_hms-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>Collection of the images that were converted by IDC was supported through the Human Tumor Atlas Network, grants 1U2CCA233262-01 "Pre-cancer atlases of cutaneous and hematologic origin (PATCH Center)" and 1U24CA233243-01 "Human Tumor Atlas Network: Data Coordinating Center" from National Cancer Institute.</p> <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. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p> <p>[2] Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jané-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe’er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H. & Human Tumor Atlas Network. <em>The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution</em>. Cell<strong> 181,</strong> 236–249 (2020). <a href="http://dx.doi.org/10.1016/j.cell.2020.03.053">http://dx.doi.org/10.1016/j.cell.2020.03.053</a></p>
DICOM converted Slide Microscopy images for the HTAN-OHSU 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=htan_ohsu">HTAN-OHSU</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> <p>The Human Tumor Atlas Network (HTAN) [2], part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. </p> <p>The overall goal of the HTAN OMS Atlas Center is to elucidate mechanisms by which metastatic breast cancers become resistant to current generation pathway- and immune checkpoint-targeted treatments. The OMS Atlas is motivated by the appreciation that these treatments are often effective in primary tumors but only transiently effective in the metastatic setting. Possible resistance mechanisms include tumor-intrinsic genomic instability and epigenomic plasticity, as well as events extrinsic to the cancer cells, including chemical and mechanical signals from the microenvironments, production of mechanical extracellular matrix barriers and/or changes in vasculature that reduce drug and/or immune cell access, nanoscale cancer cell-microenvironment interactions that reduce drug efficacy, and a plethora of immune resistance mechanisms, such as loss of HLA expression and antigen presentation, and immune exhaustion. These mechanisms likely vary between patients and within individual patients and change with time as tumors respond to therapeutic attack. The OMS Atlas will focus on elucidating resistance mechanisms in two specific current generation clinical trial scenarios: (a) hormone receptor-positive breast cancer (HRBC) undergoing treatment with a CDK4/6 inhibitor in combination with endocrine therapy and (b) triple negative breast cancer (TNBC) undergoing treatment with a PARP inhibitor and an immunomodulatory agent.</p> <p>Please see the <a href="https://humantumoratlas.org/hta9/"> HTAN-OHSU <em> </em> </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://docs.google.com/document/d/1seSp9yo8mqMK5sFXd4LEf_t_4SEofZi-1p-M75R1lhk/edit#heading=h.ybiv33wzts40"> HTAN Publication Policy <em> </em> </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>htan_ohsu-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>htan_ohsu-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>htan_ohsu-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>Collection of the images that were converted by IDC was supported through the Human Tumor Atlas Network, grants 1U2CCA233280-01 "Omic and Multidimensional Spatial Atlas of Metastatic Breast and Prostate Cancers" and 1U24CA233243-01 "Human Tumor Atlas Network: Data Coordinating Center" from National Cancer Institute.</p> <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. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p> <p>[2] Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jané-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe’er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H. & Human Tumor Atlas Network. <em>The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution</em>. Cell<strong> 181,</strong> 236–249 (2020). <a href="http://dx.doi.org/10.1016/j.cell.2020.03.053">http://dx.doi.org/10.1016/j.cell.2020.03.053</a></p>
DICOM converted Slide Microscopy images for the HTAN-VANDERBILT 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=htan_vanderbilt">HTAN-VANDERBILT</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> <p>The Human Tumor Atlas Network (HTAN) [2], part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. </p> <p>Colorectal cancer (CRC) is among the top three most prevalent cancers in global incidence and mortality. Most of these cancers develop from pre-cancerous adenomas. There is an unmet need to develop new preventive strategies and risk stratification models to decrease incidence, improve early detection, and prevent deaths from CRC.</p> <p>We believe that the ability to provide the most effective precision diagnostics and preventive strategies can only be achieved with single-cell analysis. As such, we will map spatial relationships across the spectrum of normal colon, early polyps, and late adenomas, including their unique stromal and microbial microenvironments to identify unique molecular phenotypes.</p> <p>Our goal will be accomplished through prospective, standardized collection and analysis of colorectal tissue, associated biospecimens, and related clinical and epidemiological data from participants undergoing colonoscopy or surgical resection. The biospecimens from these participants will be used for single-cell RNA sequencing, whole exome sequencing, multiplex immunofluorescence, species-specific bacterial fluorescence in situ hybridization, and other approaches. Finally, the information from these approaches will be integrated to develop a single-cell pre-cancer atlas with defined molecular phenotypes for dissemination to the broader scientific community.</p> <p>Please see the <a href="https://humantumoratlas.org/hta11/"> HTAN-Vanderbilt <em> </em> </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://docs.google.com/document/d/1seSp9yo8mqMK5sFXd4LEf_t_4SEofZi-1p-M75R1lhk/edit#heading=h.ybiv33wzts40"> HTAN Publication Policy <em> </em> </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>htan_vanderbilt-idc_v15-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>htan_vanderbilt-idc_v15-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>htan_vanderbilt-idc_v15-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>Collection of the images that were converted by IDC was supported through the Human Tumor Atlas Network, grants 1U2CCA233291-01 "Integrative Single-Cell Atlas of Host and Microenvironment in Colorectal Neoplastic Transformation" and 1U24CA233243-01 "Human Tumor Atlas Network: Data Coordinating Center" from National Cancer Institute.</p> <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. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p> <p>[2] Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jané-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe’er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H. & Human Tumor Atlas Network. <em>The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution</em>. Cell<strong> 181,</strong> 236–249 (2020). <a href="http://dx.doi.org/10.1016/j.cell.2020.03.053">http://dx.doi.org/10.1016/j.cell.2020.03.053</a></p>
DICOM converted Slide Microscopy images for the Cancer Moonshot Biobank initiative collections
<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 <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=cmb_aml&collection_id=cmb_crc&collection_id=cmb_gec&collection_id=cmb_lca&collection_id=cmb_mel&collection_id=cmb_mml&collection_id=cmb_pca">here</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>The <a href="https://moonshotbiobank.cancer.gov/">Cancer Moonshot Biobank</a> (CMB) is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving <a href="https://www.cancer.gov/publications/dictionaries/cancer-terms/def/standard-of-care">standard of care cancer treatment</a> at multiple <a href="https://ncorp.cancer.gov/">NCI Community Oncology Research Program (NCORP)</a> sites.</div> <div> <p>CMB program is organized into multiple cancer-specific collections. Digital pathology images for each of those collections were converted into DICOM representation by the IDC team and are shared via IDC.</p> <p>1. <a href="https://doi.org/10.7937/PCTE-6M66">CMB-AML</a> (acute myeloid leukemia cancer)<br>2. <a href="https://doi.org/10.7937/DJG7-GZ87">CMB-CRC</a> (colorectal cancer)<br>3. <a href="https://doi.org/10.7937/E7KH-R486">CMB-GEC</a> (gastroesophageal cancer)<br>4. <a href="https://doi.org/10.7937/3CX3-S132">CMB-LCA</a> (lung cancer)<br>5. <a href="https://doi.org/10.7937/GWSP-WH72">CMB-MEL</a> (melanoma)<br>6. <a href="https://doi.org/10.7937/SZKB-SW39">CMB-MEL</a> (multiple myeloma)<br>7. <a>CMB-PCA</a> (prostate cancer)</p> </div> </div> <p>Digital pathology images, augmented with the metadata describing their content, were converted into DICOM Whole Slide Microscopy (SM) representation [2,3] using custom open source scripts and tools as described in [4]. </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> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code><collection_id>-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code><collection_id>-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code><collection_id>-idc_v19-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> <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. 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. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] National Electrical Manufacturers Association (NEMA). <em>DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD</em>. at <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8></p> <p>[3] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. <em>J. Pathol. Inform.</em> <strong>9,</strong> 37 (2018).</p> <p>[4] Clunie, D., Fedorov, A. & Herrmann, M. D. <em>ImagingDataCommons/idc-wsi-conversion: Initial release</em>. (Zenodo, 2023). doi:10.5281/ZENODO.8240154</p>
GTEx: DICOM converted whole slide hematoxylin and eosin stained images from the Genotype-Tissue Expression (GTEx) Project
<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=gtex" target="_blank" rel="noopener">GTEx</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> <p>The<a href="https://commonfund.nih.gov/GTEx"> Genotype-Tissue Expression (GTEx) Project</a> established a data resource and tissue bank to study the relationship between genetic variants and gene expression in multiple human tissues and across individuals. The project included contributions from numerous groups with diverse expertise in biospecimen collection and processing, pathology review, molecular analysis, and data management. The contributors are collectively called the GTEx Consortium.</p> <p>GTEx collected a total of 26,468 unique tissue samples from 50+ different tissue types, from 956 healthy postmortem donors. The standardized biospecimen collection and analysis practices applied during the study served to minimize preanalytical variability associated with specimen-related factors and their potential impact on analytic endpoints. Each GTEx tissue was divided into two tissue blocks, one for histology and one for molecular analysis; both tissue blocks were preserved in PAXgene Tissue Fixative (Qiagen) solution for 6 to 24 hours, followed by PAXgene Tissue Stabilizer (Qiagen) as specified in the project-specific<a href="https://biospecimens.cancer.gov/resources/sops/library.asp"> standard operating procedures</a>. Tissue blocks were processed and embedded in paraffin at the GTEx central repository at the Van Andel Institute (MI) and hematoxylin and eosin–stained slides were generated from all GTEx donors. Digitally scanned whole slide images of PAXgene-fixed/stabilized, paraffin-embedded tissue sections were created using Aperio Scanscope software (Leica Biosystems). The digital images were then reviewed and annotated by one of four board-certified pathologists assigned to the GTEx study. There are a total of 25,503 digital histology images in the GTEx collection.</p> <p>GTEx was supported by the NIH Common Fund (2010 – 2019). Additional resources include the<a href="https://gtexportal.org/home/biobank"> GTEx Biobank</a>, the<a href="https://gtexportal.org/home/"> GTEx Portal</a>, and the full dataset at<a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2"> dbGaP</a> (accession number phs000424).</p> <p>Please refer to the listed GTEx publications below for more details [2-7]. </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>gtex-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>gtex-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>gtex-idc_v19-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> <div>Please acknowledge the GTEx Consortium in any published work that includes the images. A sample statement for the acknowledgment of the Genotype-Tissue Expression (GTEx) Project dataset(s) follows.</div> <p>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (<a href="http://commonfund.nih.gov/GTEx" target="_blank" rel="noopener">commonfund.nih.gov/GTEx</a>). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute of MIT and Harvard. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported with supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941& MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina - Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822).</p> <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. 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. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] Sobin, L., Barcus, M., Branton, P. A., Engel, K. B., Keen, J., Tabor, D., Ardlie, K. G., Greytak, S. R., Roche, N., Luke, B., Vaught, J., Guan, P. & Moore, H. M. Histologic and quality assessment of genotype-Tissue Expression (GTEx) research samples: A large postmortem tissue collection. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0467-OA">10.5858/arpa.2023-0467-OA</a></p> <p>[3] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).</p> <p>[4] GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).</p> <p>[5] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).</p> <p>[6] Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., Guan, P., Korzeniewski, G. E., Lockhart, N. C., Rabiner, C. A., Rao, A. K., Robinson, K. L., Roche, N. V., Sawyer, S. J., Segrè, A. V., Shive, C. E., Smith, A. M., Sobin, L. H., Undale, A. H., Valentino, K. M., Vaught, J., Young, T. R., Moore, H. M. & GTEx Consortium. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).</p> <p>[7] Branton, P. A., Sobin, L., Barcus, M., Engel, K. B., Greytak, S. R., Guan, P., Vaught, J. & Moore, H. M. Notable histologic findings in a ‘normal’ cohort: The National Institutes of Health Genotype-Tissue Expression (GTEx) project. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0468-OA">10.5858/arpa.2023-0468-OA</a></p>
µCT scan data: stack of DICOM images
<p>MicroCT or µCT scan data: stack of DICOM images of the crab <i>Secretanella</i> sp. (ALMNH:Paleo:6522) from an upper Campanian methane seep limestone in Pennington County, South Dakota.</p>
CT DICOM studies from: In vivo measurements of lung volumes in ringed seals: insights from biomedical imaging
Open the record for dataset details and reuse information.
Deduplicate DICOM Contour
<p>Deduplicate DICOM Contour</p>
Rt-Cloud rtAttenPenn Sample 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 the r<a href="https://github.com/brainiak/rtAttenPenn_cloud">tAttenPenn project</a>. A previous version of this same data was uploaded for a different rt-cloud <a href="https://github.com/amennen/amygActivation">sample project</a> <a href="https://doi.org/10.5281/zenodo.3862783">(zenodo link here)</a>. For the current sample project, we are additionally including a T1w anatomical scan for real-time registration. All brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
DICOM Dose and Structure Demo
<p>DICOM Dose and Structure Demo</p>
Demo DICOM plan
<p>Demo DICOM plan</p>
PyMedPhys - Dicom-to-delivery NPCA file
<p>https://github.com/pymedphys/pymedphys/issues/1047</p>
Magnetic Resonance Fingerprinting DICOM Validation Datasets for Real-Time Automated Quality Control for Quantitative MRI
<p>12 DICOM validation datasets (in DICOMDIR .zip format) from two Magnetic Resonance Fingerprinting sequences at two slice thickness across three test-retest sets per configuration. Also included are the resulting vial extraction reports and data for each dataset. </p>
Mapeo Sistemático de la Literatura: Desarrollo de un Sistema Interactivo de Interconexión y Análisis de Imágenes DICOM con Retroalimentación Médica
Open the record for dataset details and reuse information.
Soil images in DICOM format including Python programs for data transformation, 3D analysis, CNN traininig, CNN analysis
Open the record for dataset details and reuse information.
Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers
Open the record for dataset details and reuse information.
Gamma regression DICOM dose comparisons
<p>A set of plans designed to be compared to each other with the gamma comparison algorithm. Used to baseline results.</p>
Example DICOM plan, dose, and log files
<p>A demo dataset for use on https://app.pymedphys.com to demonstrate mapping a log file to a DICOM file, calculating the dose, and taking a gamma and mudensity analysis of the results.</p>
Dataset of DICOM and MatLab images for review purposes relative to JAPPL-00449-2018
<p>See rebuttal note.</p>
Example DICOM RT Structure
<p>Example DICOM RT Structure</p>
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