radar-generalist/RADAR-auxiliary-data

RADAR: Preprocessed Anatomical Masks for Merlin CT Data

This dataset provides preprocessed anatomical segmentation masks for the Merlin abdominal CT training set, generated by TotalSegmentator and post-processed for use with the RADAR framework. These masks enable anatomy-aware vision–language pretraining without any additional manual annotation.

Overview

RADAR is a generalist vision–language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs. A key component of its training pipeline is the use of anatomical segmentation masks to establish spatial correspondences between image regions and organ-level textual descriptions. This dataset releases the preprocessed masks used in the RADAR+ experiments on the Merlin dataset.

Mask Generation Pipeline

The masks were produced through the following steps:

1. Automatic segmentation: Each Merlin training CT volume was processed with TotalSegmentator, producing voxel-level labels for 104 anatomical structures.
2. Structure merging: The 104 fine-grained labels were consolidated into 36 main anatomical structures relevant to abdominal CT diagnosis.
3. Spacing resampling: Masks were resampled to a uniform spacing of 1 × 1 × 5 mm (matching the resampled CT images).
4. Format: Each mask is saved as a single-channel NIfTI file (.nii.gz), where each voxel value corresponds to an organ index (1–36; 0 = background).

36 Anatomical Structures

Organ Index Table

Index	Structure	Index	Structure
1	Adrenal gland	19	Inferior vena cava
2	Aorta	20	Kidney
3	Erector spinae muscle	21	Liver
4	Brain	22	Lung
5	Clavicle	23	Pancreas
6	Large bowel	24	Portal vein
7	Duodenum	25	Pulmonary artery
8	Esophagus	26	Rib
9	Face	27	Sacrum
10	Femur	28	Scapula
11	Gallbladder	29	Small bowel
12	Gluteus muscle	30	Spleen
13	Heart	31	Stomach
14	Hip joint	32	Trachea
15	Humerus	33	Bladder
16	Iliac artery	34	Cervical vertebrae
17	Iliac vena	35	Lumbar vertebrae
18	Iliopsoas muscle	36	Thoracic vertebrae

Dataset Structure

data/merlin_data_train_full/resized_masks/
├── part_00/
│   ├── <patient_id_a>.nii.gz
│   └── ...
├── part_01/
│   ├── <patient_id_b>.nii.gz
│   └── ...
└── part_02/
    ├── <patient_id_c>.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 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:

# 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/
│   ├── <patient_id>.nii.gz              # Merlin CT volumes resampled to 1×1×5 mm
│   └── ...
└── resized_masks/
    ├── <patient_id>.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 and resample the CT volumes to 1 × 1 × 5 mm spacing.
2. Download the RADAR model checkpoints from HuggingFace.

Citation

If you use these masks in your research, please cite:

@article{radar2026,
  title={RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis},
  author={...},
  year={2026}
}

License

This dataset is released under CC BY-NC-SA 4.0.