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title | Data Access |
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| Data AccessChoosing 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.
Data Type | Download all or Query/Filter | License |
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Images, Segmentations (DICOM, | 57.8GB)Image Removed Image Removed
| Med ABD Lymph Annotations (ZIP) | 58.4 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_03-31-2023.tcia?api=v2 |
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label | Search |
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url | https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=CT%20Lymph%20Nodes |
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(Download requires the NBIA Data Retriever) | | Med ABD Lymph Annotations (txt, mps, ZIP, 704 files, 307 kB) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&modificationDate=1435166807156&api=v2 |
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| | Med Lymph Candidate Nodes (ZIP, 1056 files, 604 kB) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_CANDIDATES.zip?version=1&modificationDate=1442245247654&api=v2 |
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| | Image Removed | Med ABD Lymph Masks ( ZIP, 1.20 MB) |
Image Removed | Click the Versions tab for more info about data releases. |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_MASKS.zip?version=1&modificationDate=1449684916503&api=v2 |
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Localtab |
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title | Detailed Description |
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| Detailed Description
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Modalities | CT, SEG | Number of | PatientsParticipants | 176 | Number of Studies | 176 | Number of Series | 176103578
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• 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. 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.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). • MEDMED_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. On 3/31/2023 (version 5) a DICOM-SEG version of these data were added to the collection.
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) |
Localtab |
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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: Tcia limited license policy |
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Info |
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| 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 |
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title | Publication Citation |
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| 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 | Info |
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title | CT Lymph Nodes Citation |
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| The Cancer Imaging Archive Team. Data From CT Lymph Nodes. doi:10.7937/K9/TCIA.2015.FTE8BOOJ |
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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, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7 |
Additional Publication Resources:The Collection authors suggest the below will give context to this dataset, please cite if you use them in your work: - 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
Other Publications Using This DataTCIA 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's Helpdesk. - Bier, B., Goldmann, F., Zaech, J. N., Fotouhi, J., Hegeman, R., Grupp, R., . . . Unberath, M. (2019). Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views. Int J Comput Assist Radiol Surg. doi: https://doi.org/10.1007/s11548-019-01975-5
- Esteban, J., Grimm, M., Unberath, M., Zahnd, G., & Navab, N. (2019). Towards Fully Automatic X-Ray to CT Registration. 11769, 631-639. doi: https://doi.org/10.1007/978-3-030-32226-7_70
- Felsner, L., Roser, P., Maier, A., & Riess, C. (2021). Comparison of methods for sensitivity correction in Talbot-Lau computed tomography. Int J Comput Assist Radiol Surg, 16(12), 2099-2106. doi: https://doi.org/10.1007/s11548-021-02487-x
- Goerres, J., Uneri, A., Jacobson, M., Ramsay, B., De Silva, T., Ketcha, M., . . . Siewerdsen, J. H. (2017). Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration. Phys Med Biol, 62(23), 9018-9038. doi: https://doi.org/10.1088/1361-6560/aa954f
- Greenspan, H., van Ginneken, B., & Summers, R. M. (2016). Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159. doi: https://doi.org/10.1109/TMI.2016.2553401
- ISKENDER, B. (2020). X-ray CT scatter correction by a physics-motivated deep neural network. (M.S. Thesis). University of Illinois at Urbana-Champaign, Retrieved from http://hdl.handle.net/2142/109445
- Iuga, A. I., Carolus, H., Hoink, A. J., Brosch, T., Klinder, T., Maintz, D., . . . Pusken, M. (2021). Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Med Imaging, 21(1), 69. doi: https://doi.org/10.1186/s12880-021-00599-z
- Krishna, P., Robinson, D. L., Bucknill, A., & Lee, P. V. S. (2022). Generation of hemipelvis surface geometry based on statistical shape modelling and contralateral mirroring. Biomechanics and Modeling in Mechanobiology. doi: https://doi.org/10.1007/s10237-022-01594-1
- Liu, F., Feng, J., Su, W., Lv, Z., Xiao, F., & Qiu, S. (2017). Normalized Euclidean Super-Pixels for Medical Image Segmentation. Paper presented at the International Conference on Intelligent Computing.
- Moshfeghifar, F., Gholamalizadeh, T., Ferguson, Z., Schneider, T., Nielsen, M. B., Panozzo, D., . . . Erleben, K. (2022). LibHip: An open-access hip joint model repository suitable for finite element method simulation. Computer Methods and Programs in Biomedicine, 226, 107140. doi: https://doi.org/10.1016/j.cmpb.2022.107140
- Reis, C., Little, B., Lee MacDonald, R., Syme, A., Thomas, C. G., & Robar, J. L. (2021). SBRT of ventricular tachycardia using 4pi optimized trajectories. J Appl Clin Med Phys. doi: https://doi.org/10.1002/acm2.13454
- Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., . . . Summers, R. M. (2014). A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. Paper presented at the Med Image Comput Comput Assist Interv.
- Sengupta, D. (2019). Deep Learning Architectures for Automated Image Segmentation. (MS). University of California, Los Angeles, Retrieved from https://escholarship.org/uc/item/6gb3k51s
- Shafiei, A., Bagheri, M., Farhadi, F., Apolo, A. B., Biassou, N. M., Folio, L. R., . . . Summers, R. M. (2021). CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1. Radiol Imaging Cancer, 3(3), e200090. doi:https://doi.org/10.1148/rycan.2021200090
- Shen, K., Quan, H., Han, J., & Wu, M. (2022). URO-GAN: An untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning. Applied Intelligence. doi: https://doi.org/10.1007/s10489-021-02976-1
- Simmons-Ehrhardt, T. (2021). Open osteology: Medical imaging databases as skeletal collections. Forensic Imaging, 26. doi: https://doi.org/10.1016/j.fri.2021.200462
- Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389/fonc.2021.637804
- Wang, H., Yi, F., Wang, J., Yi, Z., & Zhang, H. (2022). RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement. IEEE Trans Med Imaging, 41(7), 1849-1861. doi:https://doi.org/10.1109/TMI.2022.3149168
- Wang, Q., Xue, W., Zhang, X., Jin, F., & Hahn, J. (2021). Pixel-wise body composition prediction with a multi-task conditional generative adversarial network. J Biomed Inform, 120, 103866. doi: https://doi.org/10.1016/j.jbi.2021.103866
- Wang, Q., Xue, W., Zhang, X., Jin, F., & Hahn, J. (2021). S2FLNet: Hepatic steatosis detection network with body shape. Comput Biol Med, 140, 105088. doi: https://doi.org/10.1016/j.compbiomed.2021.105088
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Localtab |
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| Version 5 (Current): Updated 2023/03/31
Data Type | Download all or Query/Filter |
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Image, Segmentations (DICOM, 58.4 GB) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_03-31-2023.tcia?api=v2 |
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Tcia button generator |
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label | Search |
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url | https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=CT%20Lymph%20Nodes |
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(Download requires the NBIA Data Retriever) | Med ABD Lymph Annotations (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1 |
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| (Current): Updated 2011/11/09&modificationDate=1435166807156&api=v2 |
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| Med Lymph Candidate Nodes (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_CANDIDATES.zip?version=1&modificationDate=1442245247654&api=v2 |
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| Med ABD Lymph Masks (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_MASKS.zip?version=1&modificationDate=1449684916503&api=v2 |
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Added DICOM version of MED_ABD_LYMPH_MASKS.zip segmentations that were previously available Version 4 : Updated 2015/12/14MED_ABD_LYMPH_MASKS.zip added via the wiki. Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_06-22-2015.tcia?version=1&modificationDate=1534787005035&api=v2 |
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(Download requires the NBIA Data Retriever) | Med ABD Lymph Annotations (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&modificationDate=1435166807156&api=v2 |
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| Med Lymph Candidate Nodes (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_CANDIDATES.zip?version=1&modificationDate=1442245247654&api=v2 |
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| Med ABD Lymph Masks (ZIP) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_MASKS.zip?version=1&modificationDate=1449684916503&api=v2 |
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| Version 3: Updated 2015/09/14MED_ABD_LYMPH_CANDIDATES.zip added via the wiki. Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) | Image Removed Image Removed | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_06-22-2015.tcia?version=1&modificationDate=1534787005035&api=v2 |
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(Download requires the NBIA Data Retriever) | Med ABD Lymph Annotations (ZIP) | Image Removed | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&modificationDate=1435166807156&api=v2 |
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| Med Lymph Candidate Nodes (ZIP) | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_CANDIDATES.zip?version=1&modificationDate=1442245247654&api=v2 |
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Version 2: Updated 2015/06/24 MED_ABD_LYMPH_ANNOTATIONS.zip added via the wiki. Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_06-22-2015.tcia?version=1&modificationDate=1534787005035&api=v2 |
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(Download requires the NBIA Data Retriever) | Med ABD Lymph | Masks (ZIP)Annotations (ZIP) | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&modificationDate=1435166807156&api=v2 |
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Version 1: Updated 2015/03/16Image data set uploaded Data Type | Download all or Query/Filter |
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Images (DICOM, 57.8GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19726546/TCIA_CT_Lymph_Nodes_06-22-2015.jnlp |
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(Download requires the NBIA Data Retriever) | Image Removed |
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