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)

Publications by the Dataset Authors

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

• 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

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.

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