The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

Note : The TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules) of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.

A database that contains brain imaging data collected on 3T MRI scanners from over 200 normally developing healthy children from birth to 18 years. The imaging data stored in the C-MIND database are DTI, HARDI, 3DT1W, 3DT2W, concurrent ASL-BOLD scans during two language tasks (Stories and Sentence-Picture Matching), Resting State fMRI and Baseline ASL scans.

Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is currently one of the powerful tools for the clinical diagnosis of dementia such as Alzheimer's Disease (AD). Meanwhile, MR imaging, being non-radioactive and having high contrast resolution, is highly accessible in clinical settings. Therefore, this dataset intends to use FDG-PET images as the Ground Truth for evaluating AD, for the development of predicting AD patients using MR images. This dataset includes an AD group and a control group (Healthy Group). The determination of the image diagnosis group is made by neurology specialists based on comprehensive judgment using clinically relevant information. Each set of data contains one set of MRI T1 images and one set of FDG-PET images. The image format is DICOM, and all images have been anonymized. To obtain the clinical information and related documentation, please contact the administrator.

The Amsterdam Ultra-high field adult lifespan database (AHEAD) consists of 105 7 Tesla (T) whole-brain structural MRI scans tailored specifically to imaging of the human subcortex, including both male and female participants and covering the entire adult life span (19-80 yrs). Data was acquired at a submillimeter resolution using a single multi-echo magnetization-prepared rapid gradient echo (MP2RAGEME) sequence, resulting in complete anatomical alignment of quantitative, R1-maps, R2*-maps, T1-maps, T1-weighted images, T2*-maps, and quantitative susceptibility mapping (QSM). Probability maps were created for five individual basal ganglia structures.

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.

The Advanced BRain Imaging on ageing and Memory (ABRIM) MRI collection includes data of 295 participants, aged between 18-80 years old. Participants underwent a multi-modal MRI protocol and behavioural examiniation.

The present collection provides both the raw and pre-processed MRI data as well as several automated and/or manual quality control indices in Brain Imaging Data Structure (BIDS) format. In addition, the collection contains information on age, sex, height, and weight.

A complete description of the MRI data collection, pre-processing steps, and data curation is provided in our preprint: https://doi.org/10.1101/2023.11.16.567360.

Please make sure to refer to the most up-to-date publication.

Data is made for available for registered users under the data user agreement (DUA) for identifiable human data - scientific use (RU-HD-SU-1.0) via: https://doi.org/10.34973/7q0a-vj19.

For more information on the data availability and corresponding (DUA), please refer to: https://data.ru.nl/.

The ABRIM behavioural collection will be released in November 2028.

Investigators at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017095 and EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD (Additional information regarding the CT projection data format in the article by Chen et al at doi: 10.1118/1.4935406), is an extended DICOM format that contains CT projection data and acquisition geometry. The de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open standardized format.

Reconstructed images, patient age, and pathology annotation are also provided for these de-identified data sets. The library consists of scans from various exam types, including non-contrast head CT scans acquired for acute cognitive or motor deficit, low-dose non-contrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions.

2016 Low Dose CT Grand Challenge

The 2016 Low Dose CT Grand Challenge, sponsored by the AAPM, NIBIB, and Mayo Clinic, used 30 contrast-enhanced abdominal CT patient scans, 10 for training and 20 for testing. Thirteen of the 20 testing datasets from the Grand Challenge were subsequently included in this larger collection of CT image and projection data (TCIA LDCT-and-Projection-data). Because of the frequency of requests received by Mayo and the AAPM for the complete 2016 Grand Challenge dataset, on September 21, 2021 all 30 cases were updated to use the same projection data format as used for the TCIA data library and made publicly available in a single location. Please refer to the READ ME file at that location for a mapping between the case ID numbers used in the 2016 Grand Challenge and the case ID numbers used in the TCIA library for the 13 cases that exist in both libraries.

Additional information about the 2016 Low Dose CT Grand Challenge can be found on the AAPM website and in the Medical Physics paper by McCollough et al.

The experimental dataset used for the refined evaluation of human fat

The Province of Ontario Neurodevelopmental Disorders (POND) Network is an Integrated Discovery Program funded by the Ontario Brain Institute and aims to understand the neurobiology of neurodevelopment disorders and translate the findings into effective new treatments. This controlled data release includes T1 weighted and T2 weighted structural MRIs, DTI, MRS, resting and task based fMRI, and MEG imaging data along with demographic, medical history data, behavioural and cognitive assessments for 682 children and youth diagnosed with various neurodevelopmental disorders as well as typically developing children and youth.

This dataset contains neuronal population activity data recorded during naturalistic behavior of common marmosets. Calcium imaging data was collected with a miniature microscope in the motor cortex of two common marmosets (identified by letters M and W).

3 types of data are available for download :
1. raw microendoscope data (multi-tiff files, 1440x1080 pixels resolution at 20Hz sampling rate. Files are divided in 4GB segments)
2. individual cell activity time series, extracted from raw data (CSV files)
3. regions of interest (ROI) of the extracted cells (png image files)

Notes :
* we analyzed all imaging data using Mosaic provided by Inscopix (Inscopix Data Processing Software, IDPS) following the "Recommended workflow" (https://support.inscopix.com/mosaic-workflow) except for acquisition-specific artifacts.
* PCA-ICA was used as the cell identification algorithm, and a serial number was affected to each identified cell (starting from 0). Before cell identification, spatial downsampling, motion correction, and df/F were applied.
* In traces.csv file, the first column indicates the time (in seconds), from the second column onward, each column represents the time series signal of each cells sorted by their serial numbers (i.e. the second column corresponds to cell #0, the third column corresponds to cell #1, and so on).
* A low-pass filter was applied to each time series signal.
* For the ROI image files (contained in th0.9.zip archive), the cell serial number is indicated in the file name (between th0.9 prefix and .png extension), but note that 0 is omitted for the first file (i.e. th0.9.png corresponds to cell #0, th0.91.png to cell #1, and so on)

Database of 141 studies which have investigated brain structure (using MRI and CT scans) in patients with bipolar disorder compared to a control group. Ninety-eight studies and 47 brain structures are included in the meta-analysis. The database and meta-analysis are contained in an Excel spreadsheet file which may be freely downloaded from this website.

This study describes a subset of the HNSCC collection on TCIA.

PURPOSE:

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Publicly shared RT data is scarce due to high complexity of RT structure data and the need for registration in time, space, and across planning sets. We here introduce an open access imaging database for patients treated with radiotherapy for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS:

2840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013 were screened. Patients with whole-body PET-CT or abdominal CT scans both before and after RT were included (n=215). Clinical data were retrieved from the MD Anderson Cancer Center custom electronic medical record system, ClinicStation. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. All files were de-identified and transferred to The Cancer Imaging Archive servers using the RSNA Clinical Trial Processor program. Files were screened for errors or residual PHI using TagSniffer and Posda Tools software, reviewed by TCIA curators, then confirmed at the parent institution.

RESULTS:

The HNSCC collection is a dataset consisting of 433,384 DICOM files from 3,225 series and 765 studies collected from 215 patients, which includes de-identified diagnostic imaging, radiation treatment planning, and follow up imaging. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.

CONCLUSION:

Recent advances in data archiving, patient de-identification, and image registration have allowed for the creation of a high quality RT-enriched imaging database within TCIA. Open access to these data allows for interinstitutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing. A related dataset describing the other component of the HNSCC collection is here: Radiomics outcome prediction in Oropharyngeal cancer DOI: 10.7937/TCIA.2020.2vx6-fy46

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Searchable book regarding molecular imaging and contrast agents (under development, in clinical trials or commercially available for medical applications) that have in vivo data (animal or human) published in peer-reviewed scientific journals prior to June 30 of 2013. 1444 agents are currently listed and there will be no more updates. Also available is a downloadable list of FDA approved contrast agents (Latest update: January 2013) and a Molecular Imaging Probes and Contrast Agents List (MIP & CA List) created by the MICAD staff by screening the PubMed / MedLine databases and other appropriate sources of such information. Only agents used in animal or human studies yielding in vivo data were selected for inclusion in the list. The list is by no means considered complete. No one imaging modality has been given preference over the others and the omission of any agent(s) or the introduction of any errors in the list is purely unintentional. The MIP & CA List is subject to the same copyright and disclaimers as the rest of the MICAD content. The database includes, but is not limited to, agents developed for positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound (US), computed tomography (CT), optical imaging, planar radiography, and planar gamma imaging. The information on each agent is summarized in a book chapter format containing several sections such as Background, Synthesis, in vitro studies, Animal Studies (with sub-sections: rodents, other non-human primate animals, and human primates), Human Studies, and References. In addition, the references are linked to PubMed for retrieval of the publication abstract. Also, each chapter contains links to resources at the National Center for Biotechnology Information (NCBI) and other relevant databases regarding the target of the imaging probe or contrast agent.

Comprehensive dataset of 100 Medical diagnostic imaging centers in Kansas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Group average map of FLAIR images in standard MNI space across 1,832 MRiShare subjects.

Collection description

This collection contains group average maps presented in the associated publication "The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students".

Subject species

homo sapiens

Modality

Structural MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

A

Purpose: To provide an annotated data set of oncologic PET/CT studies for the development and training of machine learning methods and to help address the limited availability of publicly available high-quality training data for PET/CT image analysis projects. This data can also be used for machine learning challenges, which is exemplified in the autoPET MICCAI 2022 competition: https://autopet.grand-challenge.org/.

Data: The anonymized publication of data was approved by the local ethics committee and data protection officer. 501 consecutive whole body FDG-PET/CT data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) as well as 513 data sets without PET-positive malignant lesions (negative controls) examined between 2014 and 2018 at the University Hospital Tübingen were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner (Siemens Biograph mCT). The imaging protocol consists of a diagnostic CT scan (mainly from skull base to mid-thigh level) with intravenous contrast enhancement in most cases, except for patients with contraindications. The following CT parameters were used: reference dose of 200 mAs, tube voltage of 120 kV, iterative reconstruction with a slice thickness of 2 - 3 mm. In addition, a whole-body FDG-PET scan was acquired 60 minutes after I.V. injection of 300-350 MBq 18F-FDG. PET data were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm with 21 subsets and 2 iterations and a gaussian kernel of 2 mm and a matrix size of 400 x 400.

All data sets were analyzed in a clinical setting by a radiologist and nuclear medicine physician in consensus identifying primary tumors and metastases in each data set. All FDG-avid lesions identified as malignant based on patient history and prior examinations were manually segmented on PET images in a slice-per-slice manner by a single reader using dedicated software (NORA imaging platform, University of Freiburg, Germany).

We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. Primary diagnosis, age and sex are provided as non-imaging information (csv). In addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file and in the hdf5 format ready to use in machine learning projects.

This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the Digital Database for Screening Mammography (DDSM) . The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer. The images have been decompressed and converted to DICOM format. Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. A manuscript describing how to use this dataset in detail is available at https://www.nature.com/articles/sdata2017177.

Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility.

For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.

Please note that the image data for this collection is structured such that each participant has multiple patient IDs. For example, participant 00038 has 10 separate patient IDs which provide information about the scans within the IDs (e.g. Calc-Test_P_00038_LEFT_CC, Calc-Test_P_00038_RIGHT_CC_1). This makes it appear as though there are 6,671 patients according to the DICOM metadata, but there are only 1,566 actual participants in the cohort.

An open source Optical Coherence Tomography Image Database containing different retinal OCT images with different pathological conditions. Please use the following citation if you use the database: Peyman Gholami, Priyanka Roy, Mohana Kuppuswamy Parthasarathy, Vasudevan Lakshminarayanan, "OCTID: Optical Coherence Tomography Image Database", arXiv preprint arXiv:1812.07056, (2018). For more information and details about the database see: https://arxiv.org/abs/1812.07056

Data accompanying the article: C. Ledig, A. Schuh, R. Guerrero, R. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, 2018. Data derived from 5074 images from the ADNI cohort: - structural segmentations (138 regions, MALPEM); - binary brain masks (pincram); - features (volumes, asymmetry, atrophy rates) and disease labels; - lists of processed images

The MaXIMA Breast Lesions Models Database is intended to provide researchers with both segmented and mathematical computer-based breast lesion models with realistic shape. The database is part of the MaXIMA project (maxima-eu.com).

Contents:

• Breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images.
• Breast cancer models obtained by segmentation of whole body and breast cadavers CT images.
• Breast cancer models based on a mathematical algorithm.
• Anonymized digital breast tomosynthesis patient images.
• Mammography images of StepWedge phantoms from different materials.
• Mammography images of breast phantoms.
• Mammography reference images.

The following articles describe the MaXIMA Breast Lesions Models Database and how it is created:

• Bliznakova, K., Dukov, N., Feradov, F., Gospodinova, G., Bliznakov, Z., Russo, P., Mettivier, G., Bosmans, H., Cockmartin, L., Sarno, A., et al, Development of breast lesions models database, Physica Medica, 64, 293-303, 2019, DOI: https://doi.org/10.1016/j.ejmp.2019.07.017
  
• Dukov, N., Bliznakova, K., Feradov, F., Buliev, I., Bosmans, H., Mettivier, G., Russo, P., Cockmartin, L., Bliznakov, Z., Models of breast lesions based on three-dimensional X-ray breast images, Physica Medica, 57, 80-87, 2019, DOI: https://doi.org/10.1016/j.ejmp.2018.12.012

Please READ and FOLLOW all of the instructions, or your request will not be considered.

Contacts: Kristina Bliznakova, MaXIMA project coordinator
kristina.bliznakova@gmail.com