Validation data sets
|
Name |
Organization |
Main objective of the data sets |
Clinical context |
Data sets (modalities and features) |
Realism vs. control |
Reference |
Availability |
Web site |
|
Truthcube |
MGH , Boston USA |
Validation of tissue deformation analysis |
None |
CT (deformed and undeformed data) |
Simulated data Control + / realism -- |
Gold standard available |
|
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RIRE |
Vanderbilt University + NLM USA |
To validate and compare rigid registration methods |
Patient brain images |
19 MR T1, T2, and PD, CT, PET neurological data sets |
Clinical data Control - / realism ++ |
GS now available Validation metrics: TRE on VOI |
Public |
|
|
BrainWebColin27 |
MNI Montreal Canada |
Validation of image processing methods |
One normal subject with no, mild, moderate or severe Multiple Sclerosis lesions added (4 different phantoms from the same anatomy). |
Simulated MR T1 T2 and PD neurological data sets normal and pathological (with one of the four phantoms and different TE, TR, noise, RF values) |
Simulated data Control + / realism + |
Gold standard available |
Public |
|
|
BrainWeb b) |
MNI Montreal Canada |
Validation of image processing methods |
20 normal subject phantoms with 20 simulated t1-weighted MRI available |
Simulated T1 weighted MR images |
Simulated data Control + / realism + |
Simulated MRs with 12 anatomical labels |
Public |
http://mouldy.bic.mni.mcgill.ca/brainweb/anatomic_normal_20.html |
|
OASIS |
Washington University Alzheimer’s Disease Research Center |
Validation of image processing algorithms trying to detect differences between MRI of AD patients and normal controls |
Patients with dementia and Alzheimer’s disease and normal controls |
T1 weighted MR images with clinical scores of 416 subjects for the cross sectional study and of 150 subjects for the longitudinal study. |
Clinical data Control - / realism ++ |
Clinical data is available |
Public |
|
|
The National Library of Medicine USA |
Mainly used for segmentation evaluation and realistic visualization |
None |
MR CT and high resolution colour data sets of the whole body |
Normal subject data Control - / realism + |
Reference available through multimodal images |
Upon request |
||
|
Yale University USA |
Suitable for many computer-based modelling and simulation calculations as well as for segmentation evaluation |
Head |
Segmented CT and MR head and torso images |
Normal subject data Control - / realism + |
Manually labelled data sets - Gold standard for segmentation |
Public |
||
|
JSRT Database |
Japanese Society of Radiological Technology Japan |
Suitable for image processing, image compression, evaluation of image display, computer-aided diagnosis |
Chest with and without nodules |
Chest XRay with manually identified nodules along with clinical information |
Clinical data Control - / realism ++ |
Manually labelled data sets - Gold standard for detection |
Upon request |
|
|
Image Science Institute NL |
Segmentation evaluation |
Chest |
247 segmented chest radiographs images |
Clinical data Control - / realism ++ |
Manually labelled lung fields, heart and clavicles - Gold standard for segmentation |
Upon request |
||
|
Image Science Institute NL |
Validation of segmentation of blood vessels in retinal images evaluation |
Subjects without and with signs of mild early diabetic retinopathy. |
40 photographs of retinal vessels |
Clinical data Control - / realism ++ |
Manually and automatic labelled retinal vessels - Gold standard for segmentation |
Upon request |
||
|
Image Science Institute NL |
2D-3D image registration validation |
Spinal |
One data set with MR, CT, 3DRX, and fluoroscopic images of 2 spinal segments |
Clinical data Control - / realism ++ |
Geometrical transformations between data sets - Gold standard for registration |
Upon request |
||
|
Segmentation of the Liver Competition 2007 |
Image Science Institute NL |
Validation of liver segmentation in CT images |
Liver: patients with tumors, metastasis and cysts in different sizes |
20 training scans and 10 testing CT images |
Clinical data Control - / realism ++ |
Manually labelled data sets - Gold standard for segmentation |
Upon request |
|
|
CAUdate SEgmentation 2007 |
Image Science Institute NL |
Validation of segmentation of caudate in MR images |
Healthy controls and subjects with Schizoptypal Personality Disorder |
18 + 15 Brain MR images |
Clinical data Control - / realism ++ |
Manually labelled data sets - Gold standard for segmentation |
Upon request |
|
|
Visages/U746 INRIA/INSERM, F |
Validation of stereoscopic reconstruction, augmented virtuality, and surface registration |
Image guided neurosurgery |
Stereoscopic images of a physical phantom with associated CT scan and calibration matrices |
Physical phantom Control + / realism - |
Computable transformations between images CS and CT CS - Gold standard for registration |
Public |
||
|
University of Iowa, USA |
Validation of non rigid image registration |
Brain images of 16 normal subjects |
T1 MR brain images |
Clinical data Control + / realism + |
Manual segmentation of 32 grey matter structures |
Upon request |
||
|
Clemson University, USA |
Validation of optical nerve identification in retina images |
31 healthy retinas and 50 retinas with disease |
81 colour images of the retina |
Clinical data Control - / realism ++ |
Manually defined location of the center point of the nerve in each image |
Public |
||
|
Massachusetts General Hospital, USA |
Validation of segmentation of brain structures in MR images |
Normal subjects and patients with tumor |
T1 MR Images of one adult subject, one 5 year old child, 38 normal subjects, and 2 patients with tumor |
Clinical data Control - / realism ++ |
Manual segmentations of different brain structures |
Upon request |
||
|
The POPI-model |
Creatis-LRMN, France |
Validation of non-rigid registration between thorax CT images. |
One patient with lung tumor |
4D CT Thorax images (10*3D CT volume) taken at different times during breathing period |
Simulated data Control + / realism + |
44 manual identified landmarks and results from their own registration method |
Public |
|
|
LPBA40 |
Laboratory of Neuro Imaging, UCLA, USA |
Validation of MRI segmentation algorithms |
Normal subjects |
40 cases with MRI coregistered and corresponding anatomic probabilistic maps |
Clinical data Control - / realism ++ |
56 anatomical structures manually segmented |
Upon request |
|
|
Alzheimer's Disease Neuroimaging Initiative (ADNI) |
Laboratory of Neuro Imaging, UCLA, USA |
Validation of image processing algorithms trying to detect differences between MRI of AD and MCI patients and normal controls |
Controls, Alzheimer's Disease, Mild Cognitive Impairment |
895 cases with MRI and PET |
Clinical data Control - / realism ++ |
Clinical data is available |
Upon request |
https://ida.loni.ucla.edu/services/Menu/IdaData.jsp?project= |
|
LID (Lung Image Database) |
Cancer Imaging Program, NCI, USA |
Validation of Computer Aided Detection of Lung cancer in CT images |
Lung cancer |
Spiral CT lung
|
Clinical data Control - / realism ++ |
Marked-up annotated lesions |
Public |
http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC/ |
|
National Biomedical Imaging Archive |
Cancer Imaging Program, NCI, USA |
Validation of Computer Aided Detection and classification of lesions from images |
Different body parts |
A large collection of medical image from different imaging protocols on different body parts. It includes LID data base. |
Clinical data Control - / realism ++ |
A lot in one place |
Public |
|
|
DDSM (Digital Database for Screening) |
University of South Florida, USA |
Validation of methods for screening mammography |
Breast cancer |
2500 studies including 2 mammograms for each breast and patient clinical information |
Clinical data Control - / realism ++ |
Manually identified suspicious regions |
Public |
|
|
Mini-MIAS (Mammographic Image Analysis Society) |
University of Essex, GB |
Validation of methods for screening mammography |
Breast cancer |
52 digitized mammograms |
Clinical data Control - / realism ++ |
Manually identified regions |
Public |
|
|
NCC-CIR |
National Cancer Center, JP |
More for clinicians. Could be used for CAD systems. |
Lung and gastro-intestinal cancer |
53 cases with medical images and diagnostic information. Images include radiographs, CT, magnetic resonance imaging (MR), endoscopic images, ultrasonographs and pathological images. |
Clinical data Control - / realism ++ |
Clinical data and diagnostic are available |
Public |
|
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MIDAS |
Kitware/UNC |
More for verification rather than validation |
Normal and pathological subjects |
A large collection of medical image from different imaging protocols on different body parts. It includes data from others databases (e.g., RIRE) |
|
A lot in one place |
Public |
|
|
Pediatric MRI Data Repository |
Multiple institutions, USA, CA |
For studying brain development |
Paediatric and young subjects |
2 sub projects : 1) 450 from 5 to 18 years-old children, 140 from 0 to 5 year-old children ; longitudinal data with MR images, DTI and MRS |
|
No real reference available. |
Upon request |
|
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BIRN (Biomedical Informatics Research Network) |
Multiple institutions, USA |
For studying inter site variations. More a place to share data. |
Healthy and pathological human brain, Cell, and Mouse atlases |
A large collection of different DTI and fMRI brain images, Microscopy and MR Images based mouse atlases |
|
No real reference available. |
Public |
|
|
International Consortium for Brain Mapping (ICBM) |
Laboratory of Neuro Imaging, UCLA, USA |
More a place to share data. |
Brain, Normal Controls |
851 cases with MRI, fMRI, MRA, DTI, PET |
|
No real reference available. |
Upon request |
https://ida.loni.ucla.edu/services/Menu/IdaData.jsp?project= |