🥞 PanTrack: Longitudinal Pancreatic Cancer Tracking Benchmark

PanTrack is a longitudinal CT benchmark for tumor lesion tracking and segmentation, introduced alongside the paper:

Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
Yannick Kirchhoff*, Maximilian Rokuss*, et al. — MICCAI 2026
[Paper] [Code & Models]

This dataset was used exclusively as a held-out out-of-distribution (OOD) test set. It was never used for training or model selection in any experiments reported in the paper. It serves as a rigorous cross-domain generalization benchmark for lesion tracking models trained on other data (e.g., autoPET/CT IV).

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Dataset Overview

Property	Value
Patients	45 with pancreatic adenocarcinoma
CT examinations	161 (2–11 per patient, mean 3.6)
Acquisition	Portal-venous phase, identical Siemens protocols, single institution
Lesion types	Pancreatic tumors + hepatic metastases (partially annotated)
Total annotated lesion instances	292 (165 pancreas, 124 liver, 3 lymph nodes)
Tracking pairs (consecutive BL→FU)	116
Vanishing lesion pairs (no FU annotation)	36 (stored as null / NaN in tracking.json)

Important caveats:

• Not all liver lesions are annotated. Only hepatic metastases deemed relevant by the radiologist are included. The absence of a liver annotation does not imply a lesion-free liver.
• Lesion indices are non-consecutive integers (e.g., 51, 81, 188, 193, 200, …). They are instance label values in the NIfTI masks and serve as stable cross-timepoint identifiers.
• Pancreatic lesions characteristically have fuzzy boundaries and subtle soft-tissue contrast, making them harder to segment than e.g. melanoma metastases.
• The diverse cohort includes patients on long-term stable chemotherapy and those with rapid progression.

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File Structure

images/                         # 161 CT scans, NIfTI format
  {PatientID}_{YYYYMMDD}_0000.nii.gz

labels/                         # 161 instance segmentation masks, NIfTI format
  {PatientID}_{YYYYMMDD}.nii.gz

totalseg/                       # 161 TotalSegmentator whole-body organ masks
  {PatientID}_{YYYYMMDD}.nii.gz

patients.json                   # patient → list of scan IDs (chronological order)
tracking.json                   # BL→FU tracking pairs with lesion center points
organ_annotations.json          # per-scan lesion metadata (organ, anatomical location)
recommended_split.json          # suggested train/val split for future experimentation

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Label Format

Labels are instance segmentation masks stored as uint16 NIfTI files. Each non-zero voxel value identifies a specific lesion instance that is consistent across all timepoints of the same patient.

• 0 = background
• Non-zero integer = lesion instance ID (non-consecutive; e.g. 51, 81, 188–200)

The same lesion in scan PanTrack_004_20221005 and PanTrack_004_20230103 will carry the same integer label value.

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tracking.json

Top-level structure: { PatientID → [ pair_dict, pair_dict, … ] }.
Each pair_dict covers one consecutive baseline→follow-up step and maps lesion_id → entry:

"PanTrack_001": [
  {
    "51": {
      "img_bl":        "PanTrack_001_20220111",
      "img_fu":        "PanTrack_001_20220711",
      "bl_point":      [235.77, 186.07, 664.71],   // lesion centroid in BL, voxel (x,y,z)
      "fu_point":      [278.76, 165.05, 629.30],   // lesion centroid in FU, voxel (x,y,z)
                                                   // null / NaN = vanishing lesion
      "fu_point_prop": [230.65, 175.46, 652.75],   // uniGradICON-propagated BL→FU point
      "merged_lesions": [51]                        // FU label IDs the lesion merged into
    }
  }
]

All coordinates are voxel-space (x, y, z), i.e. to convert to array index use (z, y, x).

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organ_annotations.json

Maps each scan to a dict of { lesion_id → { organ, location, display_text } }:

"PanTrack_004_20230920": {
  "195": { "organ": "liver",    "location": "Leber Segment V",   "display_text": "PAM03 Leber Segment V" },
  "200": { "organ": "liver",    "location": "Leber Segment VI",  "display_text": "PAM08 Leber Segment VI" },
  "188": { "organ": "pancreas", "location": "Pankreas Kopf",     "display_text": "PAT01 Pankreas Kopf" }
}

Note: the lesion IDs in this file use a different numbering scheme than the label mask instance values. Use tracking.json as the primary source for cross-timepoint correspondence.

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recommended_split.json

A single-fold split (109 train / 52 val scans) provided for researchers who wish to fine-tune or train on PanTrack data. Not used in the original paper since the full dataset was treated as a zero-shot OOD test set.

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Citation

If you use PanTrack, please cite:

@inproceedings{kirchhoff2026pantrack,
  title     = {Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking},
  author    = {Kirchhoff, Yannick and Rokuss, Maximilian and Mertens, Daniel Philipp and
               F{\"u}ller, David and Hamm, Benjamin and Schreyer, Andreas and
               Ritter, Oliver and Maier-Hein, Klaus},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
  year      = {2026}
}
@inproceedings{rokuss2024longitudinal,
  title={Longitudinal segmentation of MS lesions via temporal Difference Weighting},
  author={Rokuss, Maximilian R and Kirchhoff, Yannick and Roy, Saikat and Kovacs, Balint and Ulrich, Constantin and Wald, Tassilo and Zenk, Maximilian and Denner, Stefan and Isensee, Fabian and Vollmuth, Philipp and Kleesiek, Jens and Maier-Hein, Klaus},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={64--74},
  year={2024},
  organization={Springer}
}