X-Ray

ChestX-Det is a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists.

CTSpine1K is a large-scale and comprehensive dataset for research in spinal image analysis. CTSpine1K is curated from the following four open sources, totalling 1,005 CT volumes (over 500,000 labeled slices and over 11,000 vertebrae) of diverse appearance variations. * COLONOG. This is a subset of the CT COLONOGRAPHY dataset related to a CT colonography trial12. * HNSCC-3DCT-RT. This sub-dataset contains three dimensional (3D) high-resolution fan-beam CT scans collected during pre-treatment, mid-treatment, and post-treatment using a Siemens 16-slice CT scanner with the standard clinical protocol for head-and-neck squamous cell carcinoma (HNSCC) patients13. These images are in DICOM format. * MSD T10. This sub-dataset comes from the 10th Medical Segmentation Decathlon14. To attain more slices containing the spine, we select the task03_liver dataset consisting of 201 cases. These images are in Neuroimaging Informatics Technology Initiative (NIfTI) format (https://nifti.nimh.nih.gov/nifti-1). * COVID-19. This sub-dataset consists of non-enhanced chest CTs from 632 patients with COVID-19 infections. The images were acquired at the point of care in an outbreak setting from patients with Reverse Transcription Polymerase Chain Reaction(RT-PCR) confirmation for the presence of SARS-CoV-215. We pick 40 scans with the images stored in NIfTI format.

X3D is a dataset containing 15 scenes and covering 4 applications for X-ray 3D reconstruction. More specifically, the X3D dataset includes the scenes of (1) medicine: jaw, leg, chest, foot, abdomen, aneurism, pelvis, pancreas, head (2) biology: carp, bonsai (3) security: box, backpack (4) industry: engine, teapot Two main tasks can be evaluated, i.e., (1) Novel View Synthesis (2) CT Reconstruction

The CheXmask Database presents a comprehensive, uniformly annotated collection of chest radiographs, constructed from five public databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest and VinDr-CXR. The database aggregates 657,566 anatomical segmentation masks derived from images which have been processed using the HybridGNet model to ensure consistent, high-quality segmentation. To confirm the quality of the segmentations, we include in this database individual Reverse Classification Accuracy (RCA) scores for each of the segmentation masks. This dataset is intended to catalyze further innovation and refinement in the field of semantic chest X-ray analysis, offering a significant resource for researchers in the medical imaging domain.

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", Sci Data 10, 576 (2023) or arXiv:2306.05907 (2023) 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 TESCAN-XRE NV, located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) 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, 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 data collection can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370. Each slice folder ‘slice00001 - slice05000’ and ‘slice05521 - slice06370’ contains three folders for each mode: ‘mode1’, ‘mode2’, ‘mode3’. In each of these folders there are the sinogram, the dark-field, and the two flat-fields for the raw data archives, or just the reconstructions and for mode2 the additional reference segmentation. The corresponding reference reconstructions and segmentations can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370. 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 github. 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

CheXlocalize is a radiologist-annotated segmentation dataset on chest X-rays. The dataset consists of two types of radiologist annotations for the localization of 10 pathologies: pixel-level segmentations and most-representative points. Annotations were drawn on images from the CheXpert validation and test sets. The dataset also consists of two separate sets of radiologist annotations: (1) ground-truth pixel-level segmentations on the validation and test sets, drawn by two board-certified radiologists, and (2) benchmark pixel-level segmentations and most-representative points on the test set, drawn by a separate group of three board-certified radiologists. The validation and test sets consist of 234 chest X-rays from 200 patients and 668 chest X-rays from 500 patients, respectively. The 10 pathologies of interest are Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged Cardiomediastinum, Lung Lesion, Lung Opacity, Pleural Effusion, Pneumothorax, and Support Devices. For more details, please see our paper, Benchmarking saliency methods for chest X-ray interpretation.

FractureAtlas is a musculoskeletal bone fracture dataset with annotations for deep learning tasks like classification, localization, and segmentation. The dataset contains a total of 4,083 X-Ray images with annotation in COCO, VGG, YOLO, and Pascal VOC format. This dataset is made freely available for any purpose. The data provided within this work are free to copy, share or redistribute in any medium or format. The data might be adapted, remixed, transformed, and built upon. The dataset is licensed under a CC-BY 4.0 license. It should be noted that to use the dataset correctly, one needs to have knowledge of medical and radiology fields to understand the results and make conclusions based on the dataset. It's also important to consider the possibility of labeling errors. Furthermore, any publication that utilizes this resource should acknowledge the original paper, and the authors are encouraged to share their code and models to assist the research community in replicating the experiments and promoting the field of medical imaging.

This dataset contains a collection of 131 X-ray CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted, retrieved in the FleX-ray lab at CWI. The dataset consists of 5 parts. It is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection". The dataset can be used to set up other CT-based experiments concerning similar objects with variations in shape and composition.

This dataset contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The dataset is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included.

We processed 241 pairs of CXR and DES soft tissue images from the JSRT dataset by performing operations like inversion and contrast adjustment to convert these images into negative formats more frequently used in clinical settings.

Description: <a href="https://gts.ai/dataset-download/spinal-vertebrae-segmentation-dataset/" target="_blank">👉 Download the dataset here</a> This dataset is comprised of high-resolution MRI scans of the spine, showcasing various degenerative and dystrophic changes, including vertebral fractures, osteophytes, and disc protrusions. It’s an invaluable resource for the development of machine learning models aimed at classifying and segmenting spinal abnormalities. Download Dataset Key Features: Comprehensive Imaging: The dataset includes 5 studies, captured from different angles, covering a range of spinal anomalies such as a focal mass in the Th7 vertebral body, fluid masses, and spondyloarthrosis. Clinical Annotations: All images are thoroughly labeled by experienced medical professionals and come with corresponding PDF reports, allowing for accurate diagnosis and reference. Diverse Conditions: The scans highlight both common and complex conditions like osteophytes, fluid masses on the anterior contour of the vertebrae, and dorsal protrusions of the Th11-Th12 disc. These features make it highly applicable for detecting fractures and classifying various spine-related abnormalities. Applications in AI and Healthcare: Fracture Detection Models: This dataset is particularly useful for training deep learning algorithms to detect vertebral fractures, nerve compression, and other dystrophic changes. Segmentation and Classification: The detailed imaging of vertebrae, intervertebral discs, and surrounding tissues enables the segmentation of critical regions for AI-driven diagnostic tools. Training Data for Degenerative Disease Detection: With a focus on conditions like spondyloarthrosis, the dataset provides robust training material for detecting degenerative spinal diseases, enhancing the accuracy of automated systems. Use Case for AI Training: Researchers and healthcare professionals can leverage this dataset to develop machine learning models for improved diagnostics in spinal health, particularly focusing on dystrophic conditions and spine anomaly classification. This dataset is sourced from Kaggle.