--- license: cc-by-nc-sa-4.0 language: - en tags: - vision-language pretraining - medical - ct diagnosis - anatomical segmentation - TotalSegmentator size_categories: - 1K.nii.gz │ └── ... ├── part_01/ │ ├── .nii.gz │ └── ... └── part_02/ ├── .nii.gz └── ... ``` - Each `.nii.gz` file shares the same patient ID as the corresponding Merlin CT image. - The mask files are aligned 1-to-1 with the resampled images in `resized_images/` (not included here; see [Merlin dataset](https://stanfordaimi.azurewebsites.net/datasets/60b9c7ff-877b-48ce-96c3-0194c8205c40) for the original CT volumes). - Before using the dataset, you need to consolidate the files into the resized_masks/ directory. Run the following command in your terminal from the root of the project: ```bash # Navigate to the target directory cd data/merlin_data_train_full/resized_masks/ # Move all .nii.gz files from subdirectories to the current folder mv part_*/ *.nii.gz . # Optional: Remove the empty part directories rm -rf part_*/ ``` ## Usage with RADAR ### Directory Layout Place the downloaded masks alongside the Merlin images to form the expected directory structure: ``` radar/data/merlin_data_train_demo/ # or merlin_data_train_full/ ├── resized_images/ │ ├── .nii.gz # Merlin CT volumes resampled to 1×1×5 mm │ └── ... └── resized_masks/ ├── .nii.gz # ← This dataset └── ... ``` ### How Masks Are Used in Training During training, the dataloader (`caption_datasets.py`): 1. Loads each CT image and its corresponding mask via MONAI transforms. 2. Pads and center-crops both to a fixed size of **96 × 256 × 384** voxels. 3. Identifies **intact organs** (those whose mask regions are fully contained within the crop, not truncated at boundaries). 4. Pairs each intact organ region with its organ-level clinical report text, enabling anatomy-aware contrastive learning. ## Data Files | File | Description | Destination | |------|-------------|-------------| | `data/merlin_data_train_full/resized_masks/*.nii.gz` | Anatomical masks for all Merlin training cases (TotalSegmentator 104 → 36 structures, resampled to 1×1×5 mm) | `radar/data/merlin_data_train_full/resized_masks/` | ## Prerequisites The anatomical masks in this dataset are designed to be used **together with** the original Merlin CT images. You will need to: 1. Download the [Merlin dataset](https://stanfordaimi.azurewebsites.net/datasets/60b9c7ff-877b-48ce-96c3-0194c8205c40) and resample the CT volumes to 1 × 1 × 5 mm spacing. 2. Download the RADAR model checkpoints from [HuggingFace](https://huggingface.co/radar-generalist/RADAR). ## Citation If you use these masks in your research, please cite: ```bibtex @article{damo-radar-2026, author = {Qi Zhang and Jianpeng Zhang and Weiwei Cao and Zilin Lu and Wanxing Chang and Haonan Ding and Cao Chen and Zhi Li and Xing Xue and Sinuo Wang and Shaoteng Zhang and Yutong Xie and Yong Xia and Qi Wu and Zhongyi Shui and Xi Li and Zhilin Zheng and Yanjie Zhou and Tony C.W. Mok and Yingda Xia and Hongkan Wang and Xianghua Ye and Tao Ma and Jie Peng and Xiaoguang Wang and Jian Ding and Yuming Gao and Huazhen Ye and Yiping Liu and Dongjie Chen and Zhaomin Ni and Jianwen Ning and Wei Zhang and Jian Liu and Chaohui Yu and Shenghong Ju and Jianfeng Zhang and Wenbo Xiao and Ling Zhang and Tingbo Liang }, title = {An expert-level generalist AI for abdominal CT diagnosis}, journal = {Science}, volume = {393}, number = {6817}, pages = {eaec6129}, year = {2026}, doi = {10.1126/science.aec6129}, URL = {https://www.science.org/doi/abs/10.1126/science.aec6129} } ``` ## License This dataset is released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).