Data Access
Data Type | Download all or Query/Filter | License |
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Images (DICOM, 57.8GB) | (Download requires the NBIA Data Retriever) | | 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. |
Detailed Description
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Modalities | CT | Number of Participants | 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. Mediastinal and abdominal lymph nodesComputer-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) |
Citations & Data Usage Policy Roth, H., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., & Summers, R. M. (2015). A new 2.5 D representation for lymph node detection in CT [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.AQIIDCNM |
Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., & Summers, R. M. (2014). A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 (pp. 520–527). Springer International Publishing. https://doi.org/10.1007/978-3-319-10404-1_65 |
Seff, A., Lu, L., Cherry, K.M., Roth, H.R., Liu, J., Wang, S., Hoffman, J., Turkbey, E.B., & Summers, R.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. (http://arxiv.org/abs/1408.3337) |
Please cite the following paper when using the segmentation masks: Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015). Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection. In Lecture Notes in Computer Science Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 (pp. 53–61). Springer International Publishing. https://doi.org/10.1007/978-3-319-24571-3_7
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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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7 |
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. |
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