Summary
This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil.
Accessing the PROSTATEx Challenge Data Sets
The data was provided in order to support a series of challenge competitions conducted by SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI). More information about the first challenge can be found at http://www.spie.org/PROSTATEx/. The second challenge is summarized at http://www.aapm.org/GrandChallenge/PROSTATEx-2/default.asp.
Acknowledgements
The prostate MR imaging was performed at the Radboud University Medical Centre (Radboudumc) in the Prostate MR Reference Center under supervision of prof. Dr. Barentsz. The Radboudumc is located in Nijmegen, The Netherlands. The dataset was collected and curated for research in computer aided diagnosis of prostate MR under supervision of Dr. Huisman, Radboudumc.
Data Access
Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option. Note: The following download links will download the entire collection. Please see the links in the Summary section above to obtain the challenge-specific data sets.
| Data Type | Download all or Query/Filter |
|---|---|
Download images (15.1 GB DICOM) |   |
Click the Versions tab for more info about data releases.
Detailed Description
Collection Statistics |
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|---|---|
Modalities | MR |
Number of Patients | 346 |
Number of Studies | 349 |
Number of Series | 18,321 |
Number of Images | 309,251 |
| Images Size (GB) | 15.1 |
PROSTATEx-1 Challenge (November 21, 2016 to February 16, 2017)
SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), conducted a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions. As part of the 2017 SPIE Medical Imaging Symposium, the PROSTATEx Challenge provided a unique opportunity for participants to compare their algorithms with those of others from academia, industry, and government in a structured, direct way using the same data sets. For more details, go to http://www.spie.org/PROSTATEx/.
- Release date of training set cases with truth: November 21, 2016
- Release date of test set cases without truth: December 12, 2016
- Submission date for participants’ test set classification output: January 16, 2017
- Challenge results released to participants: January 20, 2017
- SPIE Medical Imaging Symposium: February 13-16, 2017
The PROSTATEx Challenge Organizers have provided the following options for download. The significance of these subsets are explained on the the challenge web site: http://spiechallenges.cloudapp.net/competitions/6.
| Data Type | Training Cohort (204 subjects) | Test Cohort (140 subjects) |
|---|---|---|
Download images (DICOM) | | |
Download Ktrans images (.mhd) | | |
Download lesion information (.zip) | | |
Download lesion reference thumbnails (.bmp) | | |
The images come in two encodings. The acquired MR is provided in DICOM encoding. Additionally Ktrans images are provided. They come in mhd format. Ktrans is a key pharmacokinetic parameter computed from the available Dynamic contrast enhanced T1-weighted series. Each patient has one study with several DICOM images and one Ktrans image. The Ktrans image is encoded in two files ProstateX-[ProxID]-Ktrans.[mhd/zraw], where ProxID is the ProstateX patient identifier. The DICOM images comprise several Series each comprising several Instances. The DICOM files are documented in the ProstateX-Images.csv file. The columns in that file encode the following:
ProxID – ProstateX patient identifier.
Name – Series Description
Studydate – Study Date
fid – Finding ID
Pos – Scanner Coordinate position of the finding
WorldMatrix – Matrix describing image orientation and scaling
ijk – image col,row,slice coordinate of finding
ImageUID – Image Identifier
TopLevel
0 - Series forms one image
1 – A set of Series forms a 4D image (e.g. Dynamic MR)
NA – Series form one image, but is part of a Level 1 4D image
SpacingBetweenSlices – Scalar Spacing between slices
VoxelSpacing – Vector with x,y,z spacing scalars
Dim – Vector with 4D dimensions of the image
DCMSerDescr – The original DICOM Series Description
DCMSerUID – The DICOM Series UID
DCMSerNum – The DICOM Series Number
InstanceUIDList – DICOM Instances that make up this series
ImageUIDList – TopLevel-NA Images the make up this Toplevel 1 image
For example, to get the ADC image of Patient ProstateX-0123 do the following. After you imported the DICOM files into your environment, go to patient ProstateX-0123 and find the series with ADC in it. In this case it is ‘ep2d_diff_tra_DYNDIST_ADC’. It has SeriesNumber 8. The DICOM images in that series form the ADC image for this challenge. Image slice j at coordinate i,j contains a finding fid. See findings for more details.
Findings
The findings are documented in the ProstateX-Findings.csv table. Documentation for the columns in that table is as follows:
ProxID – ProstateX patient identifier
fid - Finding ID
pos - Scanner Coordinate position of the finding
ClinSig – Identifier available in training set that identifies whether this is a clinically significant finding. Either the biopsy GleasonScore was 7 or higher. Findings with a PIRADS score 2 were not biopsied and are not considered clinically significant. In our center the occurrence of clinically significant cancer in PIRADS 2 lesions is less than 5%.
Citations & Data Usage Policy
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.
Please be sure to include the following citations in your work if you use this data set:
Data Citation
Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman. "ProstateX Challenge data", The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9TCIA.2017.MURS5CL
Publication Citation
G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092.
TCIA Citation
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
Other Publications Using This Data
TCIA maintains a list of publications which leverage our data. At this time we are not aware of any publications based on this data. If you have a publication you'd like to add please contact the TCIA Helpdesk.
Version 1 (Current): Updated 2017/03/29
| Data Type | Download all or Query/Filter |
|---|---|
Download images (15.1 GB DICOM) | |