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CT-LYMPH-NODES

The Cancer Imaging Archive

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

DOI: 10.7937/K9/TCIA.2015.AQIIDCNM | Data Citation Required | 1k Views | 30 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Abdomen and Mediastinum Human 176 SEG, CT, Measurement, Other, Segmentation Lymphadenopathy (non-cancer) 58.42GB Image Analyses, Organ segmentations Public, Complete 2023/03/31

Summary

This collection consists of Computed Tomography (CT) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the National Institutes of Health, Clinical Center. Radiologists at the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory labeled a total of 388 mediastinal lymph nodes in CT images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients.

The collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. Automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in CT and to their varying sizes, poses, shapes and sparsely distributed locations. This data set is made available to make direct comparison to other detection methods in order to advance the state of the art.

Data Access

Version 5: Updated 2023/03/31

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

Title Data Type Format Access Points Subjects Studies Series Images License
Images, Segmentations SEG, CT DICOM
Download requires NBIA Data Retriever
 176 176 352 110,179 CC BY 3.0
Mediastinal and Abdominal Lymph Annotations Measurement, Other TXT, MPS, and ZIP 704 CC BY 3.0
Med Lymph Candidate Nodes Measurement ZIP 1,056 CC BY 3.0
Med ABD Lymph Masks Segmentation NIFTI and ZIP 176 176 CC BY 3.0
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Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

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

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)

Acknowledgements

  • 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.

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication Citation

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

The Collection authors suggest the below will also 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

Publication Citation

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

Research Community Publications

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.

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Additional Publications Related to this Work

The Collection authors suggest the below will also 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 TCIA’s Helpdesk.

Previous Versions

Version 4: Updated 2015/12/14

MED_ABD_LYMPH_MASKS.zip added via the wiki.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever
Med ABD Lymph Annotations ZIP
Med Lymph Candidate Nodes ZIP
Med ABD Lymph Masks ZIP

Version 3: Updated 2015/09/14

MED_ABD_LYMPH_CANDIDATES.zip  added via the wiki.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever
Med ABD Lymph Annotations ZIP
Med Lymph Candidate Nodes ZIP

Version 2: Updated 2015/06/24

MED_ABD_LYMPH_ANNOTATIONS.zip   added via the wiki.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever
Med ABD Lymph Annotations ZIP

Version 1: Updated 2015/03/16

Image data set uploaded

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever