MerlinPlus provides longitudinal metadata and AI-generated organ segmentation masks to support multi-tumor early detection research, with radiologist-made tumor masks forthcoming.

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2026-06-29

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Why it is worth attention

It extends the Merlin CT foundation model by releasing de-identified longitudinal metadata and organ masks, enabling temporal analysis and training of segmentation models for multi-tumor detection.

Who it is for

  • Medical imaging AI researchers
  • Researchers working on CT organ/tumor segmentation
  • Clinicians and data scientists studying longitudinal disease progression
  • Dataset creators and benchmarking groups

Use cases

  • Training and evaluating organ segmentation models
  • Longitudinal model training and evaluation using repeated patient scans
  • Multi-tumor early detection research with combined organ and tumor masks
  • Developing temporal biomarkers from CT scan intervals

Strengths

  • Provides longitudinal metadata (patient IDs, dates) to support temporal modeling
  • AI-made organ segmentation masks already released on HuggingFace
  • Rich metadata includes age, race, sex, scanner details, contrast, and kVp
  • Part of the Merlin ecosystem with a Nature publication and ongoing support

Considerations

  • Radiologist-made tumor segmentation masks are not yet released (planned soon)
  • Paper describing the dataset and methods is coming soon, not yet available
  • No code or model weights are provided in this repository, only datasets

README quick start

MerlinPlus

[MICCAI 2026] Merlin Plus

Planned release:

  • AI-made organ segmentation masks (used to train R-Super): released at https://huggingface.co/datasets/AbdomenAtlas/MerlinPlus
  • Longitudinal metadata: released in merlin_longitudinal_metadata.csv in this repository. This metadata provides de-identified patient IDs and examination dates, enabling you to identify CT scans from the same patient and calculate the time interval between examinations. It therefore supports longitudinal model training and evaluation. We additionally provide de-identified patient age, race, sex, scanner manufacturer and model, CT voxel spacing, contrast status, contrast phase, kVp, and X-ray tube current.
  • Radiologist-made tumor segmentation masks: soon

Paper:

Coming soon!

Citation:

If you use the code, data or methods in this repository, please cite all papers below:

@article{bassi2025scaling,
  title={Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks},
  author={Bassi, Pedro RAS and Zhou, Xinze and Li, Wenxuan and P{\l}otka, Szymon and Chen, Jieneng and Chen, Qi and Zhu, Zheren and Prz{\k{a}}do, Jakub and Hamac{\i}, Ibrahim E and Er, Sezgin and others},
  journal={arXiv preprint arXiv:2510.14803},
  year={2025}
}

@article{blankemeier_kumar2026merlin,
  author = {Blankemeier, Louis and Kumar, Ashwin and Cohen, Joseph Paul and Liu, Jiaming and Liu, Longchao and Van Veen, Dave and Gardezi, Syed Jamal Safdar and Yu, Hongkun and Paschali, Magdalini and Chen, Zhihong and Delbrouck, Jean-Benoit and Reis, Eduardo and Holland, Robbie and Truyts, Cesar and Bluethgen, Christian and Wu, Yufu and Lian, Long and Jensen, Malte Engmann Kjeldskov and Ostmeier, Sophie and Varma, Maya and Valanarasu, Jeya Maria Jose and Fang, Zhongnan and Huo, Zepeng and Nabulsi, Zaid and Ardila, Diego and Weng, Wei-Hung and Amaro Junior, Edson and Ahuja, Neera and Fries, Jason and Shah, Nigam H. and Zaharchuk, Greg and Willis, Marc and Yala, Adam and Johnston, Andrew and Boutin, Robert D. and Wentland, Andrew and Langlotz, Curtis P. and Hom, Jason and Gatidis, Sergios and Chaudhari, Akshay S.},
  title   = {Merlin: a computed tomography vision-language foundation model and dataset},
  journal = {Nature},
  year    = {2026},
  doi     = {10.1038/s41586-026-10181-8},
  url     = {https://doi.o

Description

[MICCAI 2026] Merlin Plus is the first large-scale public CT dataset with radiologist-created tumor masks across 9 organs (spleen, bladder, gallbladder, stomach, duodenum, prostate, adrenal glands, esophagus, and uterus), adding 1,153 per-voxel tumor masks to the Stanford Merlin dataset.

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