| --- |
| license: cc-by-4.0 |
| size_categories: |
| - n<1K |
| task_categories: |
| - image-segmentation |
| pretty_name: LUTSeg |
| tags: |
| - medical |
| - wound-care |
| - semantic-segmentation |
| - longitudinal |
| - multi-expert |
| - leprosy |
| - image |
| --- |
| |
| # LUTSeg |
|
|
| **LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation** |
| contains pixel-level tissue annotations for longitudinal, leprosy-related chronic |
| ulcer images. |
|
|
| - **Paper:** [LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation](https://huggingface.co/papers/2608.25866) |
| - **Code:** [carlosh93/TiSage](https://github.com/carlosh93/TiSage) |
|
|
| The accompanying TiSage paper was selected as a **Spotlight** at the Eleventh |
| ISIC Skin Image Analysis Workshop @ MICCAI 2026. |
|
|
| ## Dataset Summary |
|
|
| - 141 images from 39 pseudonymized patients |
| - 111 training images and 30 validation images, split at the patient level |
| - Longitudinal acquisition over 21 months |
| - Binary wound masks and six-class tissue masks, including background |
| - A 46-image gold-standard subset from 9 patients annotated by five clinicians |
| - Per-clinician masks and inter-rater agreement artifacts for the gold subset |
|
|
| LUTSeg reorganizes images collected in the SIMATEC project by patient and time |
| and adds new expert tissue labels. The source images originate from the |
| [CO2Wounds dataset](https://data.mendeley.com/datasets/nkw5gx57hw/1) described |
| in the [original study](https://doi.org/10.1016/j.compbiomed.2023.107753). |
|
|
| ## Labels |
|
|
| | ID | Class | |
| |---:|---| |
| | 0 | Background | |
| | 1 | Epithelial tissue | |
| | 2 | Slough | |
| | 3 | Granulation tissue | |
| | 4 | Necrotic tissue | |
| | 5 | Other | |
|
|
| `Masks/` stores single-channel tissue IDs. `Wound_Masks/` stores binary masks |
| with values 0 and 255. `Masks_RGB/` provides visualizations and must not be used |
| as training targets. |
|
|
| ## Repository Layout |
|
|
| ```text |
| Images/ source RGB images |
| Masks/ tissue-label masks |
| Masks_RGB/ colorized tissue-mask visualizations |
| Wound_Masks/ binary wound masks |
| metadata.jsonl paired files and sample metadata |
| train.txt, val.txt full-supervision patient-level split |
| splits/ full, 1/4, 1/8, and 1/16 paper splits |
| gold_standard/ multi-expert masks and agreement artifacts |
| checksums.sha256 release integrity manifest |
| ``` |
|
|
| The identifiers in paths and metadata are dataset-internal pseudonyms. They are |
| not hospital identifiers or patient names. Clinician and reviewer identifiers |
| are also permanent pseudonyms. |
|
|
| ## Download and Use with TiSage |
|
|
| Download directly into the location expected by TiSage: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="ksanchez84/LUTSeg", |
| repo_type="dataset", |
| local_dir="data/LUTSeg", |
| ) |
| ``` |
|
|
| The resulting `data/LUTSeg/Images`, `data/LUTSeg/Masks`, `train.txt`, and |
| `val.txt` paths work directly with the code at |
| [carlosh93/TiSage](https://github.com/carlosh93/TiSage). |
|
|
| For Hugging Face Datasets, `metadata.jsonl` uses multiple `*_file_name` fields |
| to pair each image with its tissue, visualization, and wound masks. The |
| `split` column distinguishes training and validation samples. |
|
|
| ## Annotation Protocol |
|
|
| Five clinicians with wound-care and skin-tissue expertise used a standardized |
| interface. Wound boundaries were delineated first, followed by pixel-level |
| annotation of epithelial, slough, granulation, necrotic, and other tissue. For |
| the 46-image gold subset, all five clinicians annotated every image. A single |
| reference mask was selected by anonymized clinician voting; ties used a |
| fixed-seed random selection. The released inter-rater files support the |
| paper-reported ICC and pairwise Dice analyses. |
|
|
| ## Ethics and Privacy |
|
|
| Acquisition followed the Declaration of Helsinki. All data were anonymized, |
| written informed consent was obtained from all participants, and the study was |
| approved by the participating hospitals' ethics committees (Approval Nos. |
| 05-21 and 30-11-25). |
|
|
| The release process removes EXIF, GPS, XMP, comments, and editing metadata from |
| all images. Visual content should still be treated as sensitive medical data |
| and handled according to applicable institutional and legal requirements. |
|
|
| ## Intended Uses and Limitations |
|
|
| LUTSeg is intended for research in wound tissue segmentation, longitudinal |
| wound analysis, annotation variability, and label-efficient learning. It is not |
| a medical device and must not be used alone for diagnosis or treatment. |
|
|
| The dataset is small, represents a specific clinical and disease context, uses |
| smartphone imagery, and contains substantial class imbalance and inter-rater |
| variability. Performance may not transfer to other populations, institutions, |
| cameras, wound etiologies, or care settings without additional validation. |
|
|
| ## License |
|
|
| LUTSeg is released under the Creative Commons Attribution 4.0 International |
| License (CC BY 4.0). Users must provide appropriate attribution and preserve the |
| dataset citation. This is the same license as the original CO2Wounds source |
| image release; LUTSeg's new annotations, splits, and metadata are distributed |
| under the same terms. |
|
|
| ## Citation |
|
|
| Please cite *LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue |
| Segmentation*, Eleventh ISIC Skin Image Analysis Workshop @ MICCAI 2026. The |
| final proceedings BibTeX will be added when the bibliographic record is public. |
