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 nodes

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.  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)

• We would like to acknowledge the individuals and institutions that have provided data for this collection: National Institutes of Health, Bethesda MD.   Special thanks to Dr. Holger R. Roth and Dr. Ronald Summers, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory , Grant Magnuson Clinical Center.
• Conversion of the segmentations into DICOM SEG representation was completed by Cosmin Ciausu using dcmqi (https://github.com/QIICR/dcmqi), assisted by Andrey Fedorov, David Clunie, and other members of the NCI Imaging Data Commons team. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 under Contract Number HHSN261201500003l from NCI.

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

TCIA maintains a list of publications which leverage our data. If you have a publication you’d like to add please contact 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 

Version 4: Updated 2015/12/14

MED_ABD_LYMPH_MASKS.zip added via the wiki.

Version 3: Updated 2015/09/14

MED_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 2015/03/16

Image data set uploaded