RAM-H1200

Dataset Summary

RAM-H1200 is a multi-task full-hand radiograph dataset for rheumatoid arthritis (RA) related image analysis. It is designed to support several clinically relevant computer vision tasks, including:

• hand bone structure segmentation
• bone erosion related segmentation
• joint localization for Sharp/van der Heijde (SvdH) scoring
• joint-level SvdH bone erosion (BE) scoring
• joint-level SvdH joint space narrowing (JSN) scoring

The dataset contains full-hand radiographs in BMP format, COCO-format annotations for segmentation and joint detection, joint-level ROI crops for scoring tasks, and study-level metadata.

Homepage

• Dataset repository: https://huggingface.co/datasets/TokyoTechMagicYang/RAM-H1200
• Benchmark repository: https://github.com/YSongxiao/RAM-H1200

DOI

• Dataset DOI: https://doi.org/<DOI_HERE>

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Supported Tasks and Applications

RAM-H1200 supports the following research tasks:

• Segmentation

  • Bone segmentation on full-hand radiographs
  • Bone erosion related segmentation

• Detection / Localization

  • Joint localization for BE-related regions
  • Joint localization for JSN-related regions

• Classification / Scoring

  • Joint-level SvdH BE score prediction
  • Joint-level SvdH JSN score prediction

Potential use cases include:

• automated RA severity assessment
• multi-task medical image analysis
• musculoskeletal structure segmentation
• joint-level radiographic scoring
• benchmarking AI systems for RA-related radiograph analysis

Dataset Structure

RAM-H1200/
|-- Segmentation/
|   |-- train/
|   |   |-- JP_HMCRD_P0001_20210615_6791_L.bmp
|   |   |-- JP_HMCRD_P0001_20210615_6791_R.bmp
|   |   |-- ...
|   |   |-- _annotations_bone_rle.coco.json
|   |   |-- _annotations_be_rle.coco.json
|   |-- val/
|   |   |-- ...
|   |   |-- _annotations_bone_rle.coco.json
|   |   |-- _annotations_be_rle.coco.json
|   |-- test/
|   |   |-- ...
|   |   |-- _annotations_bone_rle.coco.json
|   |   |-- _annotations_be_rle.coco.json
|-- SvdH_Scoring/
|   |-- SvdH_BE_Scoring/
|   |   |-- train/
|   |   |   |-- JP_HMCRD_P0001_20210615_6791_L/
|   |   |   |   |-- CMC-T.bmp
|   |   |   |   |-- IP.bmp
|   |   |   |   |-- L.bmp
|   |   |   |   |-- MCP-I.bmp
|   |   |   |   |-- ...
|   |   |   |-- _annotations_be_joint_detection.coco.json
|   |   |   |-- _annotation_be_scores.json
|   |   |-- val/
|   |   |   |-- ...
|   |   |   |-- _annotations_be_joint_detection.coco.json
|   |   |   |-- _annotation_be_scores.json
|   |   |-- test/
|   |   |   |-- ...
|   |   |   |-- _annotations_be_joint_detection.coco.json
|   |   |   |-- _annotation_be_scores.json
|   |-- SvdH_JSN_Scoring/
|   |   |-- train/
|   |   |   |-- JP_HMCRD_P0001_20210615_6791_L/
|   |   |   |   |-- CMC-M.bmp
|   |   |   |   |-- CMC-R.bmp
|   |   |   |   |-- CMC-S.bmp
|   |   |   |   |-- MCP-I.bmp
|   |   |   |   |-- ...
|   |   |   |-- _annotations_jsn_joint_detection.coco.json
|   |   |   |-- _annotation_jsn_scores.json
|   |   |-- val/
|   |   |   |-- ...
|   |   |   |-- _annotations_jsn_joint_detection.coco.json
|   |   |   |-- _annotation_jsn_scores.json
|   |   |-- test/
|   |   |   |-- ...
|   |   |   |-- _annotations_jsn_joint_detection.coco.json
|   |   |   |-- _annotation_jsn_scores.json
|-- Metadata.xlsx

Data Organization

1. Segmentation

The Segmentation/ directory contains full-hand radiographs in BMP format, organized into train, val, and test splits.

A typical filename looks like:

JP_HMCRD_P0001_20210615_6791_L.bmp

This naming scheme generally encodes:

• country or source prefix
• acquisition center
• anonymized patient identifier
• study date
• image identifier
• hand side (L for left, R for right)

Each split contains two COCO-format annotation files:

• _annotations_bone_rle.coco.json
• _annotations_be_rle.coco.json

Bone Segmentation Annotations

_annotations_bone_rle.coco.json stores segmentation masks using COCO RLE encoding. The annotation categories include anatomical structures such as:

• Capitate
• Hamate
• Lunate
• Scaphoid
• Trapezium
• Trapezoid
• Radius
• Ulna
• MC1--MC5
• PP1--PP5
• DP1--DP5

The annotation file also contains some additional categories related to non-bony structures or acquisition artifacts, such as soft tissue or implants.

Example COCO annotation:

{
  "id": 1,
  "image_id": 0,
  "category_id": 30,
  "bbox": [14.0, 198.0, 852.0, 1233.0],
  "area": 515212.0,
  "segmentation": {
    "size": [1431, 893],
    "counts": "..."
  }
}

Bone Erosion Related Segmentation Annotations

_annotations_be_rle.coco.json provides segmentation annotations related to bone erosion patterns. The category set includes:

• Fusion
• Non-SvdH-BE
• OP
• SvdH-BE-50
• SvdH-BE-90

These annotations are also stored in COCO RLE format.

2. SvdH BE Scoring

The SvdH_Scoring/SvdH_BE_Scoring/ directory contains ROI crops for bone erosion scoring. Each case is stored in a separate folder named by a case identifier.

Example:

JP_HMCRD_P0001_20210615_6791_L/

A typical BE case folder contains 16 ROI images corresponding to joints or anatomical regions such as:

• CMC-T.bmp
• IP.bmp
• L.bmp
• Tm.bmp
• R.bmp
• U.bmp
• MCP-T.bmp
• MCP-I.bmp
• MCP-M.bmp
• MCP-R.bmp
• MCP-S.bmp
• PIP-I.bmp
• PIP-M.bmp
• PIP-R.bmp
• PIP-S.bmp

Each split also includes:

• _annotations_be_joint_detection.coco.json
• _annotation_be_scores.json

BE Joint Detection

_annotations_be_joint_detection.coco.json stores joint localization annotations in COCO format. The categories map to BE-relevant joints or regions, including:

• R
• U
• L
• CMC-T
• S
• Tm
• IP
• MCP-T
• MCP-I
• MCP-M
• MCP-R
• MCP-S
• PIP-I
• PIP-M
• PIP-R
• PIP-S

BE Score Labels

_annotation_be_scores.json stores ground-truth joint-level BE scores indexed by full image filename.

Example:

{
  "JP_HMCRD_P0167_20110314_3497_L.bmp": {
    "BE_MCP-T": 0,
    "BE_MCP-I": 1,
    "BE_MCP-M": 0,
    "BE_MCP-R": 0,
    "BE_MCP-S": 0,
    "BE_IP": 0,
    "BE_PIP-I": 0,
    "BE_PIP-M": 0,
    "BE_PIP-R": 1,
    "BE_PIP-S": 1,
    "BE_CMC-T": 0,
    "BE_Tm": 1,
    "BE_S": 0,
    "BE_L": 0,
    "BE_U": 0,
    "BE_R": 0
  }
}

3. SvdH JSN Scoring

The SvdH_Scoring/SvdH_JSN_Scoring/ directory contains ROI crops for joint space narrowing scoring.

A typical JSN case folder contains 15 ROI images corresponding to:

• CMC-M.bmp
• CMC-R.bmp
• CMC-S.bmp
• SC.bmp
• SR.bmp
• STT.bmp
• MCP-T.bmp
• MCP-I.bmp
• MCP-M.bmp
• MCP-R.bmp
• MCP-S.bmp
• PIP-I.bmp
• PIP-M.bmp
• PIP-R.bmp
• PIP-S.bmp

Each split also includes:

• _annotations_jsn_joint_detection.coco.json
• _annotation_jsn_scores.json

JSN Joint Detection

_annotations_jsn_joint_detection.coco.json stores COCO-format joint localization annotations. Categories include:

• CMC-M
• CMC-R
• CMC-S
• SC
• SR
• STT
• MCP-T
• MCP-I
• MCP-M
• MCP-R
• MCP-S
• PIP-I
• PIP-M
• PIP-R
• PIP-S

JSN Score Labels

_annotation_jsn_scores.json stores ground-truth joint-level JSN scores indexed by full image filename.

Example:

{
  "JP_HMCRD_P0167_20110314_3497_L.bmp": {
    "JSN_MCP-T": 2,
    "JSN_MCP-I": 0,
    "JSN_MCP-M": 0,
    "JSN_MCP-R": 0,
    "JSN_MCP-S": 0,
    "JSN_PIP-I": 0,
    "JSN_PIP-M": 0,
    "JSN_PIP-R": 0,
    "JSN_PIP-S": 0,
    "JSN_STT": 0,
    "JSN_SC": 0,
    "JSN_SR": 0,
    "JSN_CMC-M": 0,
    "JSN_CMC-R": 0,
    "JSN_CMC-S": 0
  }
}

Metadata

The file Metadata.xlsx contains study-level metadata. Key columns include:

• Mapped Image Stem
• StudyID
• Normalized PatientID
• isRA
• Sex
• Age
• Center
• BirthDate
• StudyDate
• PixelSpacing
• ImageSize
• LR

These fields provide normalized identifiers, demographic information, acquisition center information, study date, image geometry, and hand laterality.

Splits

RAM-H1200 is distributed with predefined splits:

• train
• val
• test

These splits are consistently provided for:

• segmentation
• BE scoring
• JSN scoring

Data Loading Notes

This repository stores raw files rather than a single tabular annotation file. Depending on the task, users will typically load data as follows:

• use BMP images together with the corresponding COCO JSON files for segmentation or detection tasks
• use per-case ROI folders together with score JSON files for BE and JSN scoring tasks
• use Metadata.xlsx for study-level metadata lookup and cohort analysis

Example Usage

Load COCO annotations

import json
from pathlib import Path

ann_path = Path("Segmentation/train/_annotations_bone_rle.coco.json")
with ann_path.open("r", encoding="utf-8") as f:
    coco = json.load(f)

print(len(coco["images"]))
print(len(coco["annotations"]))
print(coco["categories"][:5])

Load BE score labels

import json
from pathlib import Path

label_path = Path("SvdH_Scoring/SvdH_BE_Scoring/train/_annotation_be_scores.json")
with label_path.open("r", encoding="utf-8") as f:
    labels = json.load(f)

sample_key = next(iter(labels))
print(sample_key)
print(labels[sample_key])

Load JSN score labels

import json
from pathlib import Path

label_path = Path("SvdH_Scoring/SvdH_JSN_Scoring/train/_annotation_jsn_scores.json")
with label_path.open("r", encoding="utf-8") as f:
    labels = json.load(f)

sample_key = next(iter(labels))
print(sample_key)
print(labels[sample_key])

Intended Uses

RAM-H1200 is intended for research and benchmarking in:

• rheumatoid arthritis radiograph analysis
• automated scoring of structural damage
• medical image segmentation
• joint localization and ROI extraction
• multi-task learning with hand radiographs

Out-of-Scope Uses

This dataset is not intended for:

• direct clinical deployment without independent validation
• standalone medical decision-making
• patient re-identification
• non-research use without checking the dataset license and ethics approvals

Source Data

RAM-H1200 consists of anonymized full-hand radiographs and derived annotations from multiple acquisition centers. It includes full-image labels, ROI-level labels, and metadata relevant to RA-related structural assessment.

Personal and Sensitive Information

The dataset uses anonymized patient and study identifiers. Metadata is limited to research-relevant study and demographic information and does not include direct personal identifiers.

Bias, Risks, and Limitations

• The dataset may reflect center-specific acquisition protocols and patient populations.
• Annotation quality depends on the consistency of expert labeling and task definitions.
• Some anatomical regions or score levels may be imbalanced.
• Models trained on this dataset may not generalize to other institutions, scanners, or populations without additional validation.
• The dataset is intended for research use, not for direct clinical diagnosis or treatment planning.

Citation

If you use RAM-H1200 in your research, please cite the dataset and the associated paper.

BibTeX

If there is an associated paper, add it here as well:

@article{ram_h1200_paper_2026,
  title   = {<PAPER_TITLE_HERE>},
  author  = {<AUTHOR_LIST>},
  journal = {<JOURNAL_OR_CONFERENCE_HERE>},
  year    = {2026},
  url     = {<PAPER_URL_HERE>}
}

Acknowledgements

We thank the annotators, clinicians, and collaborating institutions who contributed to the collection, curation, and quality control of RAM-H1200.

Contact

For questions, issues, or collaboration inquiries, please contact:

• Songxiao Yang, Yafei Ou
• syang(at)ok.sc.e.titech.ac.jp, yafei.ou(at)riken.jp
• https://yafeiou.github.io/RAM10K