A new 2.5 D representation for lymph node detection in CT (CT Lymph Nodes)

Detailed Description

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)

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

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 Data

TCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add please contact the TCIA 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 5 (Current): Updated 2023/03/31

Added DICOM version of MED_ABD_LYMPH_MASKS.zip segmentations that were previously available

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