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VIA Group Public Databases

Documented image databases are essential for the development of quantitative image analysis tools especially for tasks of computer-aided diagnosis (CAD). In collaboration with the I-ELCAP group we have established two public image databases that contain lung CT images in the DICOM format together with documentation of abnormalities by radiologists. Please access the links below for more details:

IN DEVELOPMENT SIMBA Framework for ECLAP Public Database

A demonstration website for the SIMBA Framework for image documetnation that is based on the ELCAP public database below. This database is still under development.

ECLAP public database of whole lung CT images

50 cases of low-dose thin-slice chest CT images with annotations for small nodules

Public Database to Address Drug Response

Over 100 cases of CT chest images illustrating the spectrum of nodule presentations together with a range of computer analysis methods.

VOLCANO'09 Nodule Change Challenge

A set of benchnmark pairs CT images of pulmonary nodules to provide a challenge for the evaluation of nodule change in size measurement methods

LIDC database size index list

Standardized nodule lists and spreadsheets for the LIDC public image database

Micro CT of Murine Lung Neoplasms

Micro-CT murin images and measurements for the following paper: M. Li, A. Jirapatnakul, M. L. Riccio, R. S. Weiss, and A. P. Reeves, "Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-ct images," PLOS ONE, 8(12):e83806, 2013

In addition to the databases shown above the VIA and ELCAP groups have made contributions to The National Cancer Institute (NCI) efforts to provide public image databases. In particular we have contributed to the following projects:

The Lung Image Database Consortium (LIDC)

The Image Database Resource Initiative (IDRI)

The Reference Image Database to Evaluate Response (RIDER)

The public databases for these projects can be accessed through the The Cancer Imaging Archive (TCIA).

As a service to the medical imaging community, we have sought to compile a list of publicly available/accessible medical image databases for the development and analysis of medical image software and computer aided detection/diagnosis tools, as well as challenges performed on various modalities.

Since there are now many more challenges and datasets publically available, as of 2014 we are no longer actively updating this list.

Public Medical Image Databases

Unrestricted Access unless otherwise noted

Chest X-ray

  • JSRT Digital Image Database. Digital Chest X-ray database with images containing lung nodules as well as negative cases, with ground truth location and diagnosis provided. JSRT Database Page
  • SCR database: Segmentation in Chest Radiographs. Digital Chest X-ray database established to facilitate comparative studies on segmentation of the lung fields, the heart and the clavicles in standard posterior-anterior chest radiographs. Image Sciences Institute: SCR database

Computed Tomography (CT)

Magnetic Resonance Images

  • BrainWeb: Simulated Brain Database. Contains simulated, 3D MR data using normal and multiple sclerosis models with different acquisition parameters. BrainWeb: SBD


  • DDSM: Digital Database for Screening Mammography. Contains a large number of cases with both normal and abnormal findings (and associated ground truth). USF DDSM Homepage
  • Mini-MIAS (Mammographic Image Analysis Society). Contains cases with and location information of the abnormality. Mini-MIAS


  • DIARETDB1 - Standard Diabetic Retinopathy Database. Database for benchmarking diabetic retinopathy detection from digital images. Offers a standardized testing protocol. Website
  • DRIVE: Digital Retinal Images for Vessel Extraction. Database established to facilitate comparative studies on segmentation of blood vessels in retinal images. Website

Virtual Colonoscopy

  • NCIA Collection: Virtual Colonoscopy - Database for colonic polyp detection from CT with MS Access relational database file to aid in case selection found at NCIA Collections Page.
    *This collection is made available from the Walter Reed Army Medical Center Virtual Colonoscopy Collection in collaboration with National Cancer Institute, NIH: please note the citation requirements on the NCIA collections page.

CT Colonography

  • TCIA Collection: ACRIN CT Colonography Collection for colonic polyp detection from CT with XLS sheets that provide polyp descriptions and their location within the colon segments ( TCIA Collections Page).


  • TCIA Collection: PET/CT phantom scan collection. A resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing. TCIA Collections Page (in transfer from NBIA)

Medical Image Analysis Challenges

Challenges, sometimes termed grand challenges provide data sets for the purpose of comparison of different analysis methods. While these challenges do provide a resource for image data they may often incur more restictive conditions on how the data may be used.

Limited Data Access unless otherwise noted, see individual challenge for details

Computed Tomography (CT)

  • EXACT09. Challenge for the automated extraction of airways from CT data. EXACT09 Homepage
  • ANODE09 Study. Challenge for the CAD detection of lung lesions from whole-lung CT data. ANODE09 Homepage
  • BIOCHANGE 2008 PILOT. Challenge for the evaluation of change measurement algorithm. BIOCHANGE 2008 Pilot Homepage *Note: Uses publicly accessible NCIA-RIDER and FDA phantom data.
  • VOLCANO'09 Nodule Change Challenge the evaluation of size change in pulmonary nodules.
  • Coronary Artery Algorithm Evaluation Framework. Challenge based on the extraction of coronary artery centerlines from CTA data. Home Page

Magnetic Resonance Images

  • MS lesion segmentation challenge 08. Challenge for the segmentation of brain lesions from MR imagery. MSseg08 Homepage
  • CAUSE07 - Caudate Segmentation Evaluation 2007. Challenge for the segmentation of the caudate nucleus from brain MRI scans. CAUSE07 Homepage


  • ROC-Retinopathy Online Challenge. Challenge based on detecting microaneurysms and dot hemorrhages for diabetic retinopathy screening. The ROC Website

Grand Challenges List