---
license: cc-by-3.0
task_categories:
  - image-segmentation
tags:
  - medical
  - ct
  - mediastinum
  - abdomen
  - lymph-node
  - lymph-node-segmentation
  - dicom
  - dicom-seg
  - nifti
  - tcia
pretty_name: CT Lymph Nodes
size_categories:
  - n<1K
---

# CT Lymph Nodes (Roth et al., NIH)

176 chest + abdomen CT scans with manually-traced voxel-wise mediastinal and
abdominal lymph node segmentations. The collection underpins the Roth 2014
detection benchmark and Seff 2015 segmentation benchmark, and remains a
widely-cited reference for thoracic-abdominal lymph node CADe work.

## Dataset Details

| Field | Value |
|---|---|
| Modality | CT |
| Body part | Mediastinum + Abdomen |
| Task | 3D binary segmentation (foreground = lymph nodes) |
| Patients | 176 (90 mediastinal + 86 abdominal) |
| Studies | 176 |
| CT series | 176 |
| SEG series (V5 DICOM-SEG) | 176 (one per patient) |
| NIfTI masks (V4) | 176 (one per patient, in `supplementary/MED_ABD_LYMPH_MASKS.zip`) |
| Images (slices) | 110,179 CT DICOM slices |
| Annotated nodes | 983 (388 mediastinal + 595 abdominal) |
| Format | DICOM (CT, DICOM-SEG) + NIfTI (legacy masks) |
| License | CC BY 3.0 |

## Anatomical Subsets

The collection is partitioned by **anatomical region** (no ML train/val/test
split is prescribed). Patient ID prefix encodes the subset:

| Subset | Patients | Annotated nodes | Measurement convention |
|---|---|---|---|
| **`MED_LYMPH_*`** | 90 | 388 | Shortest axis only (RECIST) |
| **`ABD_LYMPH_*`** | 86 | 595 | Longest + shortest axis (axial view) |

## Mask Sources

The collection ships three companion annotation archives. **`MASKS`** is the
recommended ground truth; the other two are pre-existing detector inputs from
the original CADe pipeline and are retained here for reproducibility.

| Source | Role | Path on HF |
|---|---|---|
| **DICOM-SEG (V5, 2023-03-31)** ★ | Recommended GT — per-patient binary lymph-node masks, aligned to source CT via per-frame `DerivationImageSequence`. | `segmentations/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm` |
| **NIfTI masks (V4, 2015-12-14)** | Same voxel-wise GT as DICOM-SEG, expressed in NIfTI. | `supplementary/MED_ABD_LYMPH_MASKS.zip` |
| **Annotations (centroids)** | Lymph-node centroid points and RECIST size measurements (`.mps`, `.txt`). | `supplementary/MED_ABD_LYMPH_ANNOTATIONS.zip` |
| **Candidates (CADe detections)** | Computer-generated positive/negative candidate lists from Cherry/Liu SPIE 2014 — detector proposals, **not** ground truth. | `supplementary/MED_ABD_LYMPH_CANDIDATES.zip` |

**Recommended GT: DICOM-SEG masks (V5).** TCIA wiki explicitly directs citing
Seff 2015 (MICCAI 2015, DOI 10.1007/978-3-319-24571-3_7) when using these
masks. The DICOM-SEG representation was added in V5 (2023) and aligns to each
source CT slice via standard DICOM cross-references. The NIfTI masks (V4)
contain the same delineations and are also provided for legacy compatibility.

The TCIA wiki notes the centroid annotations and the masks were produced
independently and their indexing is **not aligned** — a loader that needs both
must key on patient ID, not on node index within a patient.

## Structure

```
images/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm        # CT
segmentations/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # DICOM-SEG
supplementary/MED_ABD_LYMPH_MASKS.zip                                  # NIfTI masks
supplementary/MED_ABD_LYMPH_ANNOTATIONS.zip                            # centroid .mps files
supplementary/MED_ABD_LYMPH_CANDIDATES.zip                             # CADe proposals
series_to_patient.json                                                 # series-level metadata
```

`PatientID` follows the pattern `MED_LYMPH_NNN` (90 cases) or
`ABD_LYMPH_NNN` (86 cases). The DICOM-SEG references its source CT via
`ReferencedSeriesSequence` (top-level CT SeriesInstanceUID) and
`PerFrameFunctionalGroupsSequence -> DerivationImageSequence ->
SourceImageSequence` (per-frame source CT SOPInstanceUID).

`series_to_patient.json` lists every CT and SEG series with `PatientID`,
`StudyInstanceUID`, `SeriesInstanceUID`, `Modality`, `SeriesDescription`,
`ImageCount`, `FileSize`, and the on-disk relative path, so loaders can index
patients without crawling the DICOM headers.

## Notes for Loaders

- **DICOM-SEG -> labelmap**: use `pydicom-seg` or `dcmqi`'s `segimage2itkimage`
  for an aligned 3D label volume. Alternatively, load the NIfTI from
  `supplementary/MED_ABD_LYMPH_MASKS.zip` and resample it to the CT grid.
- **No prescribed split**: a single train split covering all 176 patients.
  Downstream tasks may carve out evaluation patients by patient ID; the
  anatomical subset (MED vs ABD) is the natural stratification axis.
- **Subset detection**: the patient-ID prefix (`MED_` vs `ABD_`) is the
  authoritative subset label.

## Source

- TCIA collection: https://www.cancerimagingarchive.net/collection/ct-lymph-nodes/
- DOI: `10.7937/K9/TCIA.2015.AQIIDCNM`
- Released: V5 on 2023-03-31 (DICOM-SEG version of masks added). Fully public,
  no registration required since 2025-07-07.

## Citation

```bibtex
@misc{roth2015ctlymphnodes,
  author    = {Roth, Holger and Lu, Le and Seff, Ari and Cherry, Kevin M. and
               Hoffman, Joanne and Wang, Shijun and Liu, Jiamin and
               Turkbey, Evrim and Summers, Ronald M.},
  title     = {A new 2.5D representation for lymph node detection in CT [Dataset]},
  year      = {2015},
  publisher = {The Cancer Imaging Archive},
  doi       = {10.7937/K9/TCIA.2015.AQIIDCNM}
}

@inproceedings{roth2014lymphnode,
  author    = {Roth, Holger R. and Lu, Le and Seff, Ari and Cherry, Kevin M. and
               Hoffman, Joanne and Wang, Shijun and Liu, Jiamin and
               Turkbey, Evrim and Summers, Ronald M.},
  title     = {A New {2.5D} Representation for Lymph Node Detection Using
               Random Sets of Deep Convolutional Neural Network Observations},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention --
               {MICCAI} 2014},
  pages     = {520--527},
  year      = {2014},
  doi       = {10.1007/978-3-319-10404-1_65}
}

@inproceedings{seff2015lymphnodemasks,
  author    = {Seff, Ari and Lu, Le and Barbu, Adrian and Roth, Holger and
               Shin, Hoo-Chang and Summers, Ronald M.},
  title     = {Leveraging Mid-Level Semantic Boundary Cues for Automated
               Lymph Node Detection},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention --
               {MICCAI} 2015},
  year      = {2015},
  doi       = {10.1007/978-3-319-24571-3_7}
}
```