--- language: - en pretty_name: RAM-H1200 size_categories: - 1K` ## 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 ```text 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: ```text 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: ```json { "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: ```text 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: ```json { "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: ```json { "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 ```python 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 ```python 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 ```python 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: ```bibtex @article{ram_h1200_paper_2026, title = {}, author = {}, journal = {}, year = {2026}, url = {} } ``` ## 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`