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-v1
Benchmark repository: https://github.com/YSongxiao/RAM-H1200

License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY NC SA 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-v1/
|-- Segmentation/
|   |-- train/
|   |   |-- JP_HMCRD_P0001_20210203_6791_L.bmp
|   |   |-- JP_HMCRD_P0001_20210203_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_20210203_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_20210203_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_20210203_6791_L.bmp

This naming scheme generally encodes:

country or source prefix
acquisition center
anonymized patient identifier
study date (de-identified via a consistent temporal offset per patient)
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:

Non-SvdH-BE
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_20210203_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_20111230_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_20111230_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
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

@misc{yang2026ramh1200,
  title={RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis},
  author={Songxiao Yang and Haolin Wang and Yao Fu and Junmu Peng and Lin Fan and Hongruixuan Chen and Jian Song and Masayuki Ikebe and Shinya Takamaeda-Yamazaki and Masatoshi Okutomi and Tamotsu Kamishima and Yafei Ou},
  year={2026},
  eprint={2605.05616},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2605.05616}
}

Suggested Citation

If you use the benchmark code or experimental settings, we also recommend citing:

@article{yang2026ram,
  title={RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis},
  author={Yang, Songxiao and Wang, Haolin and Fu, Yao and Tian, Ye and Kamishima, Tamotsu and Ikebe, Masayuki and Ou, Yafei and Okutomi, Masatoshi},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  year={2026}
}

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

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yhlin/HXR_measurement

Image Segmentation • Updated Jul 30 • 1

Paper for TokyoTechMagicYang/RAM-H1200-v1

RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis

Paper • 2605.05616 • Published May 7 • 1