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282 results for “image segments”
PENGWIN Task 2: Pelvic Fragment Segmentation on Synthetic X-ray Images
<p>The <a href="https://pengwin.grand-challenge.org/">PENGWIN segmentation challenge</a> is designed to advance the development of automated pelvic fracture segmentation techniques in both 3D CT scans (Task 1) and 2D X-ray images (Task 2), aiming to enhance their accuarcy and robustness. The full 3D dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning devices. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts, and are available <a href="https://doi.org/10.5281/zenodo.10927452">here</a>. From this 3D data, we have generated high-quality, realistic X-ray images and corresponding 2D labels from the CT data using <a href="https://github.com/arcadelab/deepdrr">DeepDRR</a>, incorporating a range of virtual C-arm camera positions and surgical tools. This dataset contains the training set for fragment segmentation in synthetic X-ray (task 2).</p> <p>The training set is derived from 100 CTs, with 500 images each, for a total of <strong>50,000 training images and segmentations</strong>. The C-arm geometry is randomly sampled for each CT within reasonable parameters for a full-size C-arm. The virtual patient is assumed to be in a head-first supine position. Imaging centers are randomly sampled within 50 mm of a fragment, ensuring good visibility. Viewing directions are sampled uniformly on the sphere within 45 degrees of vertical. Half of the images (IDs XXX_0250 - XXX_0500) contain up to 10 simulated K-wires and/or orthopaedic screws oriented randomly in the field of view.</p> <p>The input images are raw intensity images without any windowing or normalization applied. It is standard practice to first apply the negative log transformation and then window each image appropriately for feeding into a model. See the included augmentation pipeline in `pengwin_utils.py` for one approach. For viewing raw images, the <a href="https://imagej.net/software/fiji/">FIJI</a> image viewer is a viable option, but it is recommended to use the included visualization functions in `pengwin_utilities.py` to first apply CLAHE normalization and save to a universally readable PNG (see example usage below).</p> <p>Because X-ray images feature overlapping segmentation maks, the segmentations have been encoded as multi-label uint32 images, where each pixel should be treated as a binary vector with bits 1 - 10 for SA fragments, 11 - 20 for LI, and 21 - 30 for RI. <strong>Thus, the raw segmentation files are not viewable with standard image viewing software.</strong> `pengwin_utilities.py` includes functions for converting to and from this format and for visualizing masks overlaid onto the original image (see below).</p> <p>To use the utilities, first install dependencies with `pip install -r requirement.txt`. Then, to visualize an image with its segmentation, you can do the following (assuming the training set has been downloaded and unzipped in the same folder):</p> <pre><code>import pengwin_utils from PIL import Image image_path = "train/input/images/x-ray/001_0000.tif" seg_path = "train/output/images/x-ray/001_0000.tif" # load image and masks image = pengwin_utils.load_image(image_path) # raw intensity image masks, category_ids, fragment_ids = pengwin_utils.load_masks(seg_path) # save visualization of image and masks # applies CLAHE normalization to the raw intensity image before overlaying segmentations. vis_image = pengwin_utils.visualize_sample(image, masks, category_ids, fragment_ids) vis_path = "vis_image.png" Image.fromarray(vis_image).save(vis_path) print(f"Wrote visualization to {vis_path}") # Obtain predicted masks, category_ids, and fragment_ids # Category IDs are {"SA": 1, "LI": 2, "RI": 3} # Fragment IDs are the integer labels from label_{category}.nii.gz, with 1 corresponding to the main fragment. pred_masks, pred_category_ids, pred_fragment_ids = masks, category_ids, fragment_ids # replace with your model # save the predicted masks for upload to the challenge # Note: cv2 does not work with uint32 images. It is recommended to use PIL or imageio.v3 pred_seg = pengwin_utils.masks_to_seg(pred_masks, pred_category_ids, pred_fragment_ids) pred_seg_path = "pred/train/output/images/x-ray/001_0000.tif" # ensure dir exists! Image.fromarray(pred_seg).save(pred_seg_path) print(f"Wrote segmentation to {pred_seg_path}")</code></pre> <p>The `pengwin_utils.Dataset` class is provided as an example of a Pytorch dataset, with strong domain randomization included to facilitate sim-to-real performance, but it is recommended to write your own as needed.</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>
A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images
<p>We have constructed a SAR (Synthetic Aperture Radar) image semantic segmentation dataset that includes 12 oceanic and atmospheric phenomena: Atmospheric Front (AF), Oceanic Front (OF), Rainfall (RF), Iceberg (IC), Sea Ice (SI), Pure Ocean Wave (POW), Wind Streak (WS), Low Wind Area (LWA), Biological Slick (BS), Micro Convective Cells (MCC), Internal Wave (IW), and Eddy.</p> <p>This dataset is built using Sentinel-1 IW and WV mode images. For WV mode data, we referenced TenGeoP-SARwv and SAR_WV_SemanticSegmentation and selected 2,383 images for semantic segmentation and annotation. For IW mode images, we incorporated some images from Tao et al.'s internal wave detection dataset. We selected 484 Sentinel-1 IW mode images obtained from 2015 to 2022 and divided them into 2,628 sub-images.</p> <p>The dataset contains a total of 5,011 image slices, with approximately 400 images for each phenomenon. All images are 16-bit .tiff files with a resolution of 100m and a size of 256x256 pixels. The images were manually annotated using the Labelme software, generating corresponding JSON files, which were then used to create the related annotation .png files.</p> <p>The updated version(V2) provides geographic information for each image.</p> <p>Thank you for your interest in our dataset. Here are the meanings of each label:</p> <p>1. BG: The unlabelled parts in JSON files are "BG" (Background)<br>2. AF: Atmospheric Front<br>3. BS: Biological Slick<br>4. I: “I” is equivalent to “IB”, representing icebergs<br>5. LWA: Low Wind Area<br>6. MCC: Micro Convective Cells<br>7. OF: Oceanic Front<br>8. POW: Pure Ocean Wave<br>9. RC: “RC” (Rain Cells) is equivalent to “RF” (Rainfall), both representing the rainfall phenomenon in the SAR image. <br>10. SI: Sea Ice<br>11. WS: Wind Streak<br>12. Eddy<br>13. IW: Internal Wave<br><em>14. HM: Represents the artificial objects appearing in the image, such as ships, aquaculture floating rafts, wind power facilities, etc.</em><br><em>15. OS: Unlike “BS”,“OS” represents mineral oil spills appearing in the SAR image (currently, there is insufficient data available for training, which will be supplemented in the future).</em></p>
Segmented Gel Electrophoresis Images used for Training GelGenie Models
<p>This repository contains gel images and corresponding hand-labelled segmentation maps (575 total) from various sources. The images have various sizes, shapes, gel contents and imaging conditions. In more detail:</p> <ul> <li>The matthew_gels (301 total), matthew_gels_2 (85 total) and nathan_gels (37 total) folders contain images originating from previous experiments in the Dunn Lab. </li> <li>The quantitation_ladder_gels (35 total) were generated specifically for the GelGenie project and contain gel ladders in each well. These were used to generate the data for Figure 1 in the main paper.</li> <li>The stella_gels_for_finetuning (26 total) folder contains gel images provided to us from Siyuan Stella Wang (Wyss Institute/Dana-Farber Cancer Institute) which we used to finetune our U-Net model and produce the results in Figure 4C of the main paper.</li> <li>The external_gels (25 total) images were gifted to us from 5 different researchers: Yichen Zhao (images generated at the University of Waterloo), Huangchen Cui (images generated at Tsinghua University), Thomas Mayer (images generated at the Technical University of Munich), Joana Reis (images generated at the Dana-Farber Cancer Institute) and Ricarda Törner (images generated at the Dana-Farber Cancer Institute). These were used as an external unseen test set for our fine-tuned model.</li> <li>The lsdb_gels (66 total) folder contains segmentation maps of images downloaded from the RGB Caps dataset, which is available from https://dbarchive.biosciencedbc.jp/en/rgp-caps/desc.html. The original images have been shared with a Creative Commons Attribution-Share Alike 2.1 Japan license. Permission was obtained from the data depositors for the training of our models on these images, as well as for the sharing of derived segmentation masks using the CC-BY license. To use this portion of the dataset, you will need to download the original images from the website, and place them alongside our segmentation masks, mirroring the same setup used for all the other datasets in this repository.</li> </ul> <p>The folders are organised as follows:</p> <ul> <li>The images, val_images and test_images contain the original images, split into training/validation/testing partitions respectively (except for the lsdb_gels dataset).</li> <li>The masks, val_masks and test_masks contain the corresponding segmentation maps for each image in the main folders (they have identical naming i.e. an image named test_image.tif will have a segmentation map labelled test_image.tif).</li> <li>The external_gels folder does not have validation or test partitions.</li> </ul> <p>The segmentation maps are 8-bit images, for which a white pixel (0) corresponds to a background pixel and a brown pixel (1) corresponds to a foreground pixel.</p> <p>This is the main dataset used to train the GelGenie models, for which the corresponding source code and GUI can be downloaded at https://github.com/mattaq31/GelGenie.</p>
TotalSegmentator MRI dataset: 616 MRI images with segmentations for 50 anatomical regions
<p>In 616 MR images we segmented 50 anatomical structures covering a majority of relevant classes for most use cases. The MR images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. Moreover, it contains some images from <a href="https://portal.imaging.datacommons.cancer.gov/" target="_blank" rel="noopener">IDC</a> for further data diversity (see column "source" in meta.csv).</p> <p>Link to a copy of this dataset on Dropbox for much quicker download: <a href="https://www.dropbox.com/scl/fi/gskhmz2n9mmlt7rcn77wo/TotalsegmentatorMRI_dataset_v200.zip?rlkey=kf1eocb6xjmqwzafuohhv19by&st=ka3m6wae&dl=0" target="_blank" rel="noopener">Dropbox Link</a></p> <p>You can find a segmentation model trained on this dataset <a href="https://github.com/wasserth/TotalSegmentator">here</a>.<br><br>More details about the dataset can be found in the corresponding <a href="https://arxiv.org/abs/2405.19492">paper</a>. Please cite this paper if you use the dataset. The CT images described in the paper can be found <a href="../doi/10.5281/zenodo.6802613" target="_blank" rel="noopener">here</a>.</p> <p>This dataset contains all 50 structures from the TotalSegmentator "total" task. It does not contain the structures of other TotalSegmentator MRI subtasks.</p> <p>This dataset was created by the department of <a href="https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin">Research and Analysis at University Hospital Basel</a>.</p> <p><strong>UPDATE</strong>: on 2025-01-21 we uploaded version 2.0.0 which increases the number of images from 298 to 616. It also contains slightly different structures.</p>
Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images
<p>Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, <i>Acute</i>, with a companion homology workflow, <i>Constellation</i>, in the PlantCV environment. <i>Acute</i> identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. <i>Constellation</i> uses a strategy of dimensionality reduction via <i>starscape</i> followed by hierarchical clustering through <i>constella </i>to identify 'constellations' of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, <i>constellaQC</i>, to test the accuracy of the clustering approach, and use it to show that the approach appropriately clusters the pseudo-landmarks derived from <i>Acute</i>, with 80-90% accuracy. We discuss the reasons for and consequences of this lack of 100% accuracy in automated workflows and suggest how to develop these functions for other phenomics datasets that may vary in dimensional complexity.</p>
Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem
<p>Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem</p> <p>Paper: https://ieeexplore.ieee.org/document/9497733/</p>
Image Data Part 2 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem
<p>Image Data Part 2 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem</p> <p>Paper: <a href="https://ieeexplore.ieee.org/document/9497733/">https://ieeexplore.ieee.org/document/9497733/</a></p>
Image Data Part 3 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem
<p>Image Data Part 3 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem</p> <p><a href="https://ieeexplore.ieee.org/document/9497733/">Paper</a>: https://ieeexplore.ieee.org/document/9497733/</p> <p> </p> <p>Other two parts: <a href="https://zenodo.org/record/5903099">Part 1</a>, <a href="https://zenodo.org/record/5903846">Part 2</a></p> <p> </p> <p> </p>
COVID Image segmentation datasets and trained model
<p>COVID CT scan datasets for segmentation and trained model for prediction created by training uNet. The referred paper is: https://www.sciencedirect.com/science/article/pii/S2666990021000069</p> <p>Origin of datasets: https://medicalsegmentation.com/covid19/</p>
Open data repository, An et al., Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images
<p>Open data repository of journal article "Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images" by Jeehye An <em>et al. </em>At time of publication of this dataset, the manuscript is still under revision.</p> <p>This dataset contains mouse T2 weighted MRI data, and manually and automated segmented ischemic stroke lesion masks for developing and evaluating a deep learning-based automated lesion segmentation. All files are in NIFTI format.<br> </p>
Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas
<p><strong>The Dataset</strong></p> <p>A collection of images of parking lots for <em>vehicle detection, segmentation, and counting</em>.<br> Each image is <em>manually</em> labeled with pixel-wise masks and bounding boxes localizing vehicle instances.<br> The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars.<br> The main peculiarity is that <em>images are taken during the day and the night</em>, showing utterly different lighting conditions.</p> <p>We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night.<br> In line with these splits we provide some annotation files:</p> <ul> <li> <p><em>train_coco_annotations.json</em> and <em>val_coco_annotations.json</em> --> JSON files that follow the golden standard MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category<em> 'car'</em>. They are suitable for vehicle detection and instance segmentation.</p> </li> <li> <p><em>train_dot_annotations.csv</em> and <em>val_dot_annotations.csv</em> --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.</p> </li> <li> <p><em>ground_truth_test_counting.csv</em> --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.</p> </li> </ul> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{Ciampi_visapp_2021, doi = {10.5220/0010303401850195}, url = {https://doi.org/10.5220%2F0010303401850195}, year = 2021, publisher = {{SCITEPRESS} - Science and Technology Publications}, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {Domain Adaptation for Traffic Density Estimation}, booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_ndispark_6560823, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}}, month = may, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6560823}, url = {https://doi.org/10.5281/zenodo.6560823} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
An Automated Method for Measuring Tree Rings Based on Super Resolution and Image Segmentation
Open the record for dataset details and reuse information.
NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation
<p>NIS3D is an image segmentation benchmark containing over 22,000 manually annotated cells.</p> <p>Find more details here: https://github.com/yu-lab-vt/NIS3D</p>
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
TCIA full and low dose CT images and volumetric normal pancreas segmentations
<p>This publicly available CT dataset was downloaded from 50 portal venous phase abdominal CT scans (3mm-slice thickness) performed in 50 patients with liver metastases— from The Cancer Imaging Archive (TCIA).These CT scans were acquired with routine radiation dose levels per standard clinical protocols on SOMATOM Definition Flash CT scanner (Siemens Healthcare, Forchheim, Germany).Each study had been postprocessed to include a second reconstructed CT dataset at a simulated 25% radiation dose level. The mean (standard deviation [SD]) radiation dose for the full dose and the reduced dose CT datasets were 14.7 mSv (8 mSv) and 3.7 mSv (2 mSv), respectively. After a curation process by radiologists, 9 of 50 cases were excluded [chronic calcific pancreatitis (n = 1), postsurgical changes in pancreas (n = 2) and pancreatic lesions (n = 6)]. Volumetric pancreas segmentations were done on all the remaining 41 cases [16 males and 25 females, mean (SD) patient age: 60.8 years (14.7 yrs)] independently by the two radiologists (R1 and R2). Segmentations were first done on the full dose CT dataset and then repeated on the reduced dose CT dataset by both R1 and R2 after a gap of at least 24 hours to allow for memory extinction.</p>
Test Dataset for 3D semantic image segmentation of the Breast, Fibrograndular Tissue, and Breast Carcinoma
Open the record for dataset details and reuse information.
TotalSegmentator-CT-Segmentations: TotalSegmentator segmentations and radiomics features for NCI Imaging Data Commons CT images
<p>This dataset contributes volumetric segmentations of the anatomic regions in a subset of CT images available from NCI Imaging Data Commons [1] (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) automatically generated using the TotalSegmentation model v1.5.6 [2]. The initial release includes segmentations for the majority of the CT scans included in the National Lung Screening Trial (NLST) collection [3], [4] already available in IDC. Direct link to open this analysis result dataset in IDC (available after release of IDC v18): <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TotalSegmentator-CT-Segmentations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TotalSegmentator-CT-Segmentations</a>. </p> <p>Specifically, for each of the CT series analyzed, we include segmentations as generated by TotalSegmentator, converted into DICOM Segmentation object format using dcmqi v1.3.0 [5], and first order and shape features for each of the segmented regions, as produced by pyradiomics v3.0.1 [6]. Radiomics features were converted to DICOM Structured Reporting documents following template TID1500 using dcmqi. TotalSegmentator analysis on the NLST cohort was executed using Terra platform [7]. Implementation of the workflow that was used for performing the analysis is available at <a href="https://github.com/ImagingDataCommons/CloudSegmentator">https://github.com/ImagingDataCommons/CloudSegmentator</a> [8].</p> <p>Due to the large size of the files, they are stored in the cloud buckets maintained by IDC, and the attached files are the manifests that can be used to download the actual files.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset and the preprint describing how it was generated [9].</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the TotalSegmentator segmentations (in DICOM SEG format) and pyradiomics measurements (in DICOM SR format) files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file. E.g., <code>idc download totalsegmentator_ct_segmentations_aws.s5cmd</code></li> </ol> <p>Other files included in the record are:</p> <ol> <li>firstorder and shape radiomics features extracted using pyradiomics, and organized one file per segmented structure (see README file in the zip file for details on how those are organized) <ol> <li>pyradiomics_features_csv.zip: saved in CSV format</li> <li>pyradiomics_features_parquet.zip: saved in Parquet format</li> </ol> </li> </ol> <h3>Support</h3> <p>If you have any questions about this dataset, or if you experience any issues, please reach out to Imaging Data Commons support via <a href="mailto:support@canceridc.dev">support@canceridc.dev</a> or (preferred) IDC Forum at <a href="https://discourse.canceridc.dev" target="_blank" rel="noopener">https://discourse.canceridc.dev</a>.</p>
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.