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3 results for “Histopathology; Nuclei segmentation;”
Segmentation of Nuclei in Histopathology Images by deep regression of the distance map
<p>This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August.<br> This dataset consists of 50 annotated images, divided into 11 patients.</p> <p> </p> <p>v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren't.</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>
HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology - Datasets
<p>This repository contains training and validation data for</p> <p><strong>HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology </strong></p> <p><strong>Accepted for Oral Presentation at MIDL2024: <a href="https://openreview.net/pdf?id=3vmB43oqIO">https://openreview.net/pdf?id=3vmB43oqIO</a> <br></strong></p> <p><strong>More information and code are available at <a href="https://github.com/digitalpathologybern/hover_next_inference" target="_blank" rel="noopener">https://github.com/digitalpathologybern/hover_next_inference</a></strong></p> <p>Modified Lizard dataset to include mitosis (lizard_mitosis.zip), mitosis dataset (mitosis_ds.zip) and a holdout eosinophil validation set (eos_eval.zip)</p> <p>mitosis_ds.zip also contains the hold-out H&E mitosis test set.</p> <p>The original lizard dataset was createdy by Simon Graham et al. and was shared under CC BY-NC-SA 4.0. The tile-based dataset can be downloaded from <a href="https://conic-challenge.grand-challenge.org/Data/">https://conic-challenge.grand-challenge.org/Data/</a> after registering for the challenge. We modify the dataset by including an additional mitosis class, however note that there are a number of mitosis which are still not (correctly annotated).</p>
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