mlsanityData to 14 Oct 2026

Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset

arXiv 2609.36436 ↗First posted 29 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Multi-cancer segmentation in computed tomography (CT) is fundamentally limited by the scarcity of tumor masks across different organs. We present Merlin Plus, the first large-scale CT dataset with radiologist-created tumor masks across 9 organs. Merlin Plus extends the Merlin dataset by adding 1,153 per-voxel tumor masks and longitudinal metadata. To create these tumor masks, we developed a report-based active-learning framework in which radiology reports identify tumor cases for annotation and support training of a tumor segmentation model. The model generates initial masks, which radiologists review and correct to produce the final masks, reducing annotation burden while maintaining high-quality annotations. Besides tumor masks, the longitudinal metadata in Merlin Plus enables temporal modeling of cancer progression. By directly addressing the major bottleneck of limited multi-cancer segmentation masks, Merlin Plus supports scalable multi-organ cancer detection, segmentation, and longitudinal analysis in CT. Dataset is available at: https://github.com/MrGiovanni/MerlinPlus

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 29 Sep 2026.

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References · 24

  1. 2021The Medical Segmentation Decathloncited by 628
  2. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  3. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  4. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 201
  5. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 190
  6. 2024Merlin: A Computed Tomography Vision-Language Foundation Model and Datasetcited by 102
  7. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 65
  8. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  9. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 36
  10. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  11. 2024The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomographycited by 26
  12. 2024Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challengecited by 23
12 more
  1. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  2. 2025VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentationcited by 19
  3. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 16
  4. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  5. 2025Learning Segmentation from Radiology Reportscited by 11
  6. 2024Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?cited by 9
  7. 2025Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Maskscited by 9
  8. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  9. 2026Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomographycited by 4
  10. 2025Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentationcited by 3
  11. 2026Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Datacited by 2
  12. 2026BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biasescited by 1

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