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Summary

Example of a high resolution segmentation. The segmentation is smooth in all directions and does not suffer from step artifactsImage Added

We created 66 high resolution segmentations for randomly chosen T2-weighted volumes of

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the SPIE-AAPM-NCI PROSTATEx Challenges (PROSTATEx). The

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high resolution segmentations were

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obtained by considering the three scan directions: for each scan direction (axial, sagittal, coronal), the gland was manually delineated by a medical student,

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followed by a review and corrections of an expert urologist. These three anisotropic segmentations were fused to one isotropic segmentation by means of shape-based

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interpolation in the following manner: (1) The signed distance transformation of the three segmentations is computed. (2) The anisotropic distance volumes are transformed into an isotropic high-resolution representation with linear interpolation. (3) By averaging the distances, smoothing and thresholding them at zero, we obtained the fused segmentation. The resulting segmentations were manually verified and corrected further by the expert urologist if necessary. Serving as ground truth for training CNNs, these segmentations have the potential to improve the segmentation accuracy of automated algorithms. By considering not only the axial scans but also sagittal and coronal scan directions, we aimed to have higher fidelity of the segmentations especially at the apex and base regions of the prostate.

For a research project about automatic high resolution prostate segmentation, our medical partners created manually 66 ground truth segmentations of the following TCIA collection: https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges

The segmentations were created by considering the axial, coronal and sagittal T2 weighted scans and have isotropic resolution.

The segmentations to standard DICOM representation were created with dcmqi .

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Acknowledgements

  • This work has been funded by the EU and the federal state of Saxony-Anhalt, Germany under grant number ZS/2016/08/80388.
Localtab Group



Localtab
activetrue
titleData Access

Data Access

Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever.

Data TypeDownload all or Query/Filter
Segmentation Images Segmentations (DICOM #, 0.# 119 GB)
Corresponding Original MR Images from PROSTATEx (DICOM, 1371.0 GB21 MB)

Patient ID List (TXT)


Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:





Localtab
titleDetailed Description

Detailed Description

Image Statistics
ModalitiesSEG
Number of Patients66
Number of Studies66
Number of Series66

Number of Images

66
Image Size (GB)0.119


Note:   Please contact help@cancerimagingarchive.net  with any questions regarding usage.




Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Public collection license

Info
titleDataset Citation
Schindele, D., Meyer, A., Von Reibnitz, D. F., Kiesswetter, V., Schostak, M., Rak, M., & Hansen, C. (20192020). High Resolution Prostate Segmentations for the ProstateX-Challenge [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.DEG7ZG1U


Info
titlePublication Citation

Meyer, A., Chlebus, G., Rak, M., Schindele, D., Schostak, M., van Ginneken, B., Schenk, A., Meine, H., Hahn, H. K., Schreiber, A., & Hansen, C. (2020). Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI. Computer Methods and Programs in Biomedicine, 105821. https://doi.org/10.1016/j.cmpb.2020.105821


Info
titleTCIA 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. DOI: 10.1007/s10278-013-9622-7

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

title
Info

Publication Citation

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Other Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.




Localtab
titleVersions

Version 1 (Current): 2020/

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09/

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Data TypeDownload all or Query/Filter
Segmentation Images Segmentations (DICOM #, 0.# 119 GB)
Corresponding Original MR Images from PROSTATEx (DICOM, 1371.0 GB21 MB)

Patient ID List (TXT)

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