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1,158 results for “cancer imaging”
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
Breast Cancer Nuclei images for DL Training + ZeroCostDL4Mic StarDist Model
<div> <p><strong>Training dataset:</strong><br>Paired microscopy images (fluorescence) and corresponding masks</p> <p>Microscopy data type: Fluorescence microscopy and masks obtained via manual correction of automatic segmentation with pre-trained StarDist model (see https://github.com/qupath/models/tree/main/stardist) </p> <p>Cells were imaged using a 20x objective with a 1x camera adapter was used in conjunction with a pco.edge 4.2 4MP camera on Pannoramic SCAN 150 scanner.</p> <p>Cell type: FFPE tissue sections were sliced from all cancer-containing paraffin blocks</p> <p>File format: .tif (8-bit for fluorescence and 16-bit for the masks)</p> <p> </p> <p><strong>StarDist Model:</strong><br>The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 100 epochs using 80 manually annotated paired images (image dimensions: (257, 257)) with a batch size of 2, an augmentation factor of 10 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 2.2.0), Keras (v 1.1.2), CSBdeep (v 0.7.2), NumPy (v 1.21.6), Cuda (v 11..1.105). The training was accelerated using a Tesla P100GPU.<br>The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. a QuPath-compatible model is also provided.</p> <p> </p> <p> </p> </div>
Oral Cancer Image Classification
<p><span>The dataset containing healthy and cancer images was obtained from Kaggle. Following an 80/10/10 split, samples were then selected for the training set (approximately 131 images), validation set (approximately 19 images), and test set (approximately 18 entirely new images). The test images were obtained from trusted medical institutes and medical databases, including online sources. </span> </p>
Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells
<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. </p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p> </p> <p> </p>
Histological images for tumor detection in gastrointestinal cancer
<p>This is a set of 11977 image patches of hematoxylin & eosin stained histological samples of human colorectal cancer. It is a subset of the data set "100,000 histological images of human colorectal cancer and healthy tissue" which is accessible at http://dx.doi.org/10.5281/zenodo.1214456. Further information on the samples is available there.</p> <p>This set contains three classes:</p> <p>ADIMUC - adipose tissue and mucus, i.e. loose non-tumor tissue</p> <p>STRMUS - stroma and muscle, i.e. dense non-tumor tissue</p> <p>TUMSTU - colorectal cancer epithelial tissue and stomach cancer epithelial tissue, i.e. tumor tissue</p> <p>All images are 512x512 px at 0.5 µm/px</p> <p>We are using this data set to train a deep neural network to detect tumor cells in histological whole slide images of colorectal and stomach cancer.</p> <p>If you use these images in your research, please consider citing our previous [1] and upcoming publication.</p> <p>For information on ethics board approval, see [1].</p> <p>-------------</p> <p>[1] http://dx.doi.org/10.1371/journal.pmed.1002730</p>
Dataset CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer
<p>Dataset for the menuscript CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer</p>
Microscopy images - "An elevated rate of whole-genome duplications associated with carcinogen exposure in Black cancer patients"
<p>This repository contains microscopy data from the manuscript "An elevated rate of whole-genome duplications associated with carcinogen exposure in Black cancer patients" by Leanne M. Brown, Ryan A. Hagenson, Tilen Koklič, Iztok Urbančič, Janez Strancar, and Jason M. Sheltzer (preprint: 10.1101/2023.11.10.23298349; accepted for publication in Nature Communications).</p> <p> </p> <p>Each zip contains the set of images from individual multi-channel multi-position time-lapse experiment with different combinations of cells exposed to one nanomaterial. Files are named as: IMGxxxx_[ExperimentCode]_ROIxx_tile[TileNumber]_[Channel]_[CellType]_t[Timepoint].tif, where each of the varying elements in [..] denotes the following:</p> <ul> <li>[ExperimentCode]: tells which material the cells were exposed to - see decoding table in the file "material-codes.xlsx"</li> <li>[TileNumber]: two xy-tiles per condition</li> <li>[Channel]: cytoplasm of epi cells (LA4), membrane (MEM), cytoplasm of imu cells and tubulin (MHSTUB), nanomaterial (NANO)</li> <li>[CellType]: mono-culture of lung epithelial cells (epi), their coculture with macrophages (epiimu)</li> <li>[Timepoint]: consecutive number of the frame in the time-lapse</li> </ul> <p> </p>
Flow cytometry data and image data -A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice
Open the record for dataset details and reuse information.
Dataset of High-Resolution Micro-CT Imaging of Tumor Invasion and Metastasis in a Murine Esophageal Cancer PDX Model
<p>This dataset features high-resolution micro-CT imaging data capturing the progression of tumor invasion and metastasis in an orthotopic patient-derived xenograft (PDX) model of esophageal cancer. Using contrast-enhanced micro-CT, we visualized detailed patterns of tumor invasion, including budding, multicellular streaming, and expansive growth, across multiple abdominal organs such as the stomach, pancreas, liver, and spleen. The dataset includes two specimens, highlighting both the primary tumor site and extensive metastases throughout the abdominal cavity. Our imaging preserved the native tissue architecture, providing a unique three-dimensional view of tumor-host interactions. This collection offers valuable insights for researchers studying the dynamics of esophageal cancer invasion and metastasis. Detailed descriptions of the micro-CT scanning parameters, image analysis, and sample preparation are provided within the dataset archive.</p>
Mammograms-Breast Cancer Images
<p><strong>ABSTRACT </strong></p> <p>This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. </p> <p><strong>Instructions: </strong></p> <p>One can use these images for experimentation on detection and analysis of breast cancer. </p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>G R Sinha, Bhagwati Charan Patel, December 27, 2019, "Mammograms-Breast Cancer Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/9f0p-qx37.</p> <p>https://ieee-dataport.org/documents/mammograms-breast-cancer-images</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Accuracy of ultrasonography and magnetic resonance imaging for preoperative staging of cervical cancer – analysis of patients from the prospective study on total mesometrial resection.
<p>This is supplementary material to the manuscript published in the Diagnostics (MDPI) journal. </p> <p>Diagnostics 2021, 11, 1749</p> <p>https://doi.org/10.3390/diagnostics11101749</p> <p>Videos present examples of ultrasound examinations of patients with cervical cancer.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 26 as continuation of dataset <strong>10.5281/zenodo.7494413.</strong> The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p> <p>Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R.</p> <p>The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file.</p> <p>The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p>
Cytological and histological images of breast cancer
<p>Data set of cytological and histological images of breast cancer.</p> <p><strong>Data set structure</strong>:</p> <ol> <li>Cytological image files (size 3264x2448 px, microscope with x40 lens used). The name of each folder contains the name of the diagnosis.</li> <li>Files of histological and corresponding immunohistochemical images of breast tissue sections (size 2048x1536 px). The name of each folder contains the name of the diagnosis. Each folder contains 1 H&E histological image and 4 folders with IHC images for 4 biomarkers: estrogen receptor (ER), progesterone receptor (PR), Ki-67 Biomarker (KI67), human epidermal growth factor receptor-2 (HER2NEU).</li> </ol>
Spatial transcriptomics images for papillary and anaplastic thyroid cancer IRIBHM dataset
<p>This dataset includes the image files for spatial transcriptomics data associated with the publication "Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers".</p>
Imaging Studies of Kidney Cancer Using 18F-VM4-037
ClinicalTrials.gov study NCT01712685. IPD Sharing: NO. Countries: 1. Publications: 4.
ZD4054 With Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI) for Prostate Cancer
ClinicalTrials.gov study NCT01119118. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Developing an Imaging-Based Tool to Identify Areas for Prostate Cancer Biopsy
ClinicalTrials.gov study NCT03585660. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Study of Intra-operative Imaging in Women With Ovarian Cancer
ClinicalTrials.gov study NCT04878094. IPD Sharing: YES. Countries: 1. Publications: 1.
Can HER2 Targeted PET/CT Imaging Identify Unsuspected HER2 Positive Breast Cancer Metastases, Which Are Amenable to HER2 Targeted Therapy?
ClinicalTrials.gov study NCT02286843. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.