Find research datasets worth reusing

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices OOD (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of the out-of-distribution slices (OOD) from the data collection described in Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels,  \(74.8\mu m^2\) each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>. The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1,001-2,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 1,001 – 2,000 from the data collection described in Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels,  \(74.8\mu m^2\) each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>. The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 1-1,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 1 – 1,000 from the data collection described in Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels,  \(74.8\mu m^2\) each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>. The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 2,001-3,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 2,001 – 3,000 from the data collection described in Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels,  \(74.8\mu m^2\) each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>. The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning: Slices 4,001-5,000 (reference reconstructions and segmentations)

This upload contains the reference reconstructions and segmentation of slices 4,001 – 5,000 from the data collection described in Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “"2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels,  \(74.8\mu m^2\) each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete dataset can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8014758">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8014766">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8014787">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8014829">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8014874">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8014907">OOD</a>. The reference reconstructions and segmentations can be found via the following links: <a href="https://doi.org/10.5281/zenodo.8017583">1-1000</a>, <a href="https://doi.org/10.5281/zenodo.8017604">1001-2000</a>, <a href="https://doi.org/10.5281/zenodo.8017612">2001-3000</a>, <a href="https://doi.org/10.5281/zenodo.8017618">3001-4000</a>, <a href="https://doi.org/10.5281/zenodo.8017624">4001-5000</a>, <a href="https://doi.org/10.5281/zenodo.8017653">OOD</a>. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on <a href="https://github.com/mbkiss/2DeteCTcodes">github</a>. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with     Maximilian.Kiss [at] cwi.nl     Felix.Lucka [at] cwi.nl

opencc-by-4.0Jul 2023View details →

LumVBCanSeg: A Lumbar Vertebral Body Cancellous Bone Segmentation Dataset

To address the lumbar vertebral body cancellous bone segmentation problem, we propose a new dataset called LumVBCanSeg. The entire dataset consists of 185 lumbar CT scans acquired using multiple CT scanners, including the two manufacturers(Philips, Siemens). All data were obtained at ShengJing Hospital of China Medical University. Cases involving vertebral fractures, metal implants, bone tumors and foreign materials were omitted. All data were resampled to an isotropic resolution of 1mm × 1mm × 1mm. Each lumbar 3D CT image has a corresponding segmentation mask annotated at voxel level using the medical image annotation package Pair. Annotations cover five lumbar vertebral body cancellous bones from L1 to L5, with labels ranging from 1 to 5. All annotations were performed by 3 physicians followed by further refinement, dismissal or approval by one physician with more than 30 years of experience in lumbar imaging.

opencc-by-4.0Jul 2023View details →

NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"

NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.

opencc-by-4.0Aug 2023View details →

Non-Contrast CT Scans Dataset for Subarachnoid Hemorrhage: Bleeding Segmentation

This dataset comprises non-contrast CT scans of patients admitted with subarachnoid hemorrhage. All scans are provided in the NIfTI format. Details of the image preprocessing, which encompasses anonymization, resampling, skull stripping, and intensity normalization, are available at <a href="https://github.com/smcch/Subarachnoid_Hemorrhage_segmentation_and_mortality_prediction">https://github.com/smcch/Subarachnoid_Hemorrhage_segmentation_and_mortality_prediction</a>. Each scan is accompanied by a corresponding NIfTI volume, showcasing the expert manual segmentation of the hemorrhage.

opencc-by-4.0Jan 2023View details →

TRACERx PHLEX test dataset, model weights & TRACERx nuclear IMC segmentation dataset

This repository contains three archives pertaining to the TRACERx PHLEX multiplexed imaging pipelines. <ol> <li>Weights for the deep-imcyto neural network models</li> <li>The PHLEX test dataset consisting of two .ome.tiff files of imaging mass cytometry data</li> <li>The TRACERx Nuclear IMC Segmentation dataset, a training dataset of IMC image tiles and ~42,000 expert labelled nuclei from diverse lung, normal and other tissue types.</li> </ol>

opencc-by-nc-nd-4.0Aug 2023View details →

RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation

We introduce the first video-based retinal vessel dataset (RVD), a collection of 635 smartphone-based videos with detailed vessel annotation. All captured videos have a frame rate of 25 frames per second, with the duration varying between 2 to 30 seconds. The total number of frames in our dataset is over 130,000. These videos are recorded from four clinics, including patients from 50 to 75 years old. More specifically, 264 males and 151 females are included in the collection process. The annotations provided in our dataset span two dimensions: spatial and temporal. In the spatial dimension, we offer three distinct levels of annotations: binary vessel masks, general vein-artery masks, and fine-grained vein-artery masks. Each kind of annotation is tailored to specific clinical purposes. In the temporal dimension, we focus on the optic disk regions of videos where the retinal vessel fluctuation normally occurs. We select and annotate frames with the maximal and minimal pulse widths as well as label the existence of spontaneous retinal venous pulsations (SVP). More detailed information can also be found on our website: https://uq-cvlab.github.io/Retinal-Video-Dataset/   If you find our RVD dataset is useful in your research, please consider cite: <pre><code>@article{MD2023RVD, title={RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation}, author={MD WAHIDUZZAMAN KHAN, Hongwei Sheng, Hu Zhang, Heming Du, Sen Wang, Minas Theodore Coroneo, Farshid Hajati, Sahar Shariflou, Michael Kalloniatis, Jack Phu, Ashish Agar, Zi Huang, Mojtaba Golzan, Xin Yu}, journal={arXiv preprint arXiv:2307.06577}, year={2023} } </code></pre>

opencc-by-4.0Sep 2023View details →

Dataset of 1000 CT Images of Thoracic Vertebrae with Segmentation

Dataset of 1000 CT Images of Thoracic Vertebrae with Segmentation   B08 - DICOM scaled to 0-255 B16 - DICOM 1:1 without Interslope B12 - DICOM scaled to 0-255 - division by 16 (4bits) BoneWnd - DICOM scaled to 0-255 - division by 8 (3bit), window of bone only

opencc-by-4.0Sep 2023View details →

Dataset of 1000 CT Images of Thoracic Vertebrae with Segmentation

Dataset of 1000 CT Images of Thoracic Vertebrae with Segmentation B08 - DICOM scaled to 0-255 B16 - DICOM 1:1 without Interslope B12 - DICOM scaled to 0-255 - division by 16 (4bits) BoneWnd - DICOM scaled to 0-255 - division by 8 (3bit), window of bone only

opencc-by-4.0Jun 2024View details →

FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas

# FireSR Dataset ## Overview **FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques. ## Dataset Structure The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring: - **S2**: Contains Sentinel-2 images.   - **pre**: Pre-fire Sentinel-2 images (high resolution).   - **post**: Post-fire Sentinel-2 images (high resolution). - **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.   - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.   - **post**: Burned area labels corresponding to post-fire conditions. - **MODIS**: Contains post-fire MODIS images (lower resolution). - **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023). - **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries. ### File Naming Convention Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where: - `CA` stands for Canada. - `<year>` is the year of the wildfire event. - `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia). - `<id>` is a unique identifier for the wildfire event. ### Directory Structure The dataset is organized as follows: ``` FireSR/ │ ├── dataset/ │   ├── S2/ │   │   ├── post/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   │   ├── pre/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   ├── mask/ │   │   ├── post/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   │   ├── pre/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   ├── MODIS/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... │   ├── LULC/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... │   ├── Daymet/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... ``` ### Spatial Resolution and Channels - **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4) - **MODIS Images**: 250 meters (Bands: B7, B2, B1) - **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned) - **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp) - **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland) **Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions: | Name | Units | Min | Max | Description | |------|-------|-----|-----|-------------| | dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. | | prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. | | srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. | | swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. | | tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. | | tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. | | vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. | **ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year. | Class Value | Land Cover Class | |-------------|------------------| | 1 | Water | | 2 | Trees | | 4 | Flooded Vegetation | | 5 | Crops | | 7 | Built Area | | 8 | Bare Ground | | 9 | Snow/Ice | | 10 | Clouds | | 11 | Rangeland | ## Usage Tutorial To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow: ### Step 1: Extract FireSR.tar.gz ```bash tar -xvf FireSR.tar.gz ``` ### Step 2: Tiling the GeoTIFF Files The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images: ```python import rasterio from rasterio.windows import Window import os def tile_image(image_path, output_dir, tile_size=128):     with rasterio.open(image_path) as src:         for i in range(0, src.height, tile_size):             for j in range(0, src.width, tile_size):                 window = Window(j, i, tile_size, tile_size)                 transform = src.window_transform(window)                 outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")                 with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:                     dst.write(src.read(window=window)) # Example usage tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/') ``` ### Step 3: Loading Data into a Machine Learning Model After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch: ```python import torch from torch.utils.data import Dataset from torchvision import transforms import rasterio class FireSRDataset(Dataset):     def __init__(self, image_dir, transform=None):         self.image_dir = image_dir         self.transform = transform         self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]     def __len__(self):         return len(self.image_paths)     def __getitem__(self, idx):         image_path = self.image_paths[idx]         with rasterio.open(image_path) as src:             image = src.read()         if self.transform:             image = self.transform(image)         return image # Example usage dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor()) dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True) ``` ## License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given. ## Contact For any questions or further information, please contact: - Name: Eric Brune - Email: ebrune@kth.se

publicJan 2022View details →

Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery

Open the record for dataset details and reuse information.

publicDec 2021View details →

KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells

Open the record for dataset details and reuse information.

publicMar 2025View details →

Dataset: Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models

Open the record for dataset details and reuse information.