Localtab |
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title | Data Access |
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| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents. Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) | | Med ABD Lymph Annotations (ZIP) | | Med Lymph Candidate Nodes (ZIP) | | Med ABD Lymph Masks (ZIP) | |
Click the Versions tab for more info about data releases. |
Localtab |
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title | Detailed Description |
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| Detailed Description | |
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Modalities | CT | Number of Patients | 176 | Number of Studies | 176 | Number of Series | 176 | Number of Images | 110,103 | Images Size (GB) | 57.8 |
The DICOM files were created from volumetric images (Analyze and NifTI) using this from ITK: http://www.itk.org/Doxygen/html/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html. Annotation files MED_ABD_LYMPH_ANNOTATIONS.zip (new 6/24/2015). The annotations include a folder for each case with text files of voxel indices, physical coordinates, size measurements and a MITK point set file (.mps), which can be visualized using the MITK workbench (Note: only release 2014.10.0 and later supports visualization of point set files using the "point set interaction plugin"). Abdominal size measurements include the longest and shortest axis in axial view of a lymph node. The shortest axis is used for the RECIST criteria. The mediastinal set only includes the shortest axis. Computer-generated candidate detections for mediastinal and abdominal lymph nodes (produced by methods in [K. Cherry et al., SPIE Med. Img. 2014] and [J. Liu et al., SPIE Med. Img. 2014]]). See attached: MED_ABD_LYMPH_CANDIDATES.zip (new 9/14/2015). MED_ABD_LYMPH_MASKS.zip (new 12/8/2015): These files contain a compressed NifTI image (*.nii.gz) for each patient with manually traced lymph node segmentations. Note: these segmentation masks were produced independently to the centroid annotations in MED_ABD_LYMPH_ANNOTATIONS.zip. There is an overlapping set of lymph nodes marked in both files but the indexing does not align. Please cite the following paper when using the segmentation masks: A Seff, L Lu, A Barbu, H Roth, HC Shin, RM Summers. Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, 53-61 (http://link.springer.com/chapter/10.1007/978-3-319-24571-3_7) |
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title | Citations & Data Usage Policy |
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| 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: Info |
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| Holger, Roth, Lu, Le, Seff, Ari, Cherry, Kevin M, Hoffman, Joanne, Wang, Shijun, … Summers, Ronald M. (2015). A new 2.5 D representation for lymph node detection in CT. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.AQIIDCNM |
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title | Publication Citation |
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| Roth, Holger R and Lu, Le and Seff, Ari and Cherry, Kevin M and Hoffman, Joanne and Wang, Shijun and Liu, Jiamin and Turkbey, Evrim and Summers, Ronald M. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. Medical Image Computing and Computer-Assisted Intervention--MICCAI 2014, p520-527, 2014. (link) |
Info |
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title | Publication Citation |
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| Seff, Ari and Lu, Le and Cherry, Kevin M and Roth, Holger R and Liu, Jiamin and Wang, Shijun and Hoffman, Joanne and Turkbey, Evrim B and Summers, Ronald M. 2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers. Medical Image Computing and Computer-Assisted Intervention--MICCAI 2014, p544-552, 2014. (link) |
Info |
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title | Publication Citation |
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| Please cite the following paper when using the segmentation masks: A Seff, L Lu, A Barbu, H Roth, HC Shin, RM Summers. Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, 53-61 (http://link.springer.com/chapter/10.1007/978-3-319-24571-3_7) |
Info |
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| 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 DataTCIA maintains a list of publications that 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. |
Localtab |
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| Version 4 (Current): Updated 2015/12/14Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) | | Med ABD Lymph Annotations (ZIP) | | Med Lymph Candidate Nodes (ZIP) | | Med ABD Lymph Masks (ZIP) | |
MED_ABD_LYMPH_MASKS.zip added via the wiki. Version 3: Updated 2015/09/14MED_ABD_LYMPH_CANDIDATES.zip added via the wiki. Version 2: Updated 2015/06/24 MED_ABD_LYMPH_ANNOTATIONS.zip added via the wiki. Version 1: Updated 2015/03/16Image data set uploaded |
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