MerlinPlus 提供了纵向元数据和 AI 生成的器官分割掩膜,以支持多肿瘤早期检测研究,后续将发布放射科医生制作的肿瘤分割掩膜。

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2026-06-29

AI 仓库情报摘要
FR-AI / ANALYSIS

为什么值得关注

它扩展了 Merlin CT 基础模型,通过发布去标识化的纵向元数据和器官掩膜,使得时间序列分析和分割模型的训练成为可能,从而助力多肿瘤检测。

适合谁使用

  • 医学影像 AI 研究人员
  • CT 器官/肿瘤分割研究人员
  • 研究纵向疾病进展的临床医生和数据科学家
  • 数据集创建者和基准测试小组

典型使用场景

  • 训练和评估器官分割模型
  • 利用同一患者的重复扫描进行纵向模型训练和评估
  • 结合器官和肿瘤掩膜进行多肿瘤早期检测研究
  • 从 CT 扫描间隔中开发时间生物学标志物

项目优势

  • 提供纵向元数据(患者 ID、日期)以支持时间建模
  • AI 生成的器官分割掩膜已在 HuggingFace 上发布
  • 丰富的元数据包括年龄、种族、性别、扫描仪参数、造影剂和 kVp
  • 作为 Merlin 生态系统的一部分,有 Nature 论文发表并持续获得支持

使用前须知

  • 放射科医生制作的肿瘤分割掩膜尚未发布(计划即将发布)
  • 描述数据集和方法的论文即将发表,目前不可用
  • 本仓库仅提供数据集,未提供代码或模型权重

README 快速开始

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

项目描述

[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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