| name | sts-skill |
| description | Claude Code Skill for MICCAI STS Challenge (2023-2026). Provides end-to-end workflows for teeth segmentation tasks including Pre-Task, Task 1/2/3, and historical challenges. Use when: starting STS challenge tasks, training UNet, evaluating segmentation masks, downloading challenge data, preparing submissions, or asking about STS competition workflow. Covers semi-supervised learning, CBCT processing, metal artifact segmentation, and instance segmentation.
|
STS-Skill
Your entry point for all MICCAI STS Challenge tasks. This skill routes you to the right workflow based on what you want to do.
How to Use
When the user mentions STS challenge, teeth segmentation, Pre-Task, or any competition task, follow this routing logic:
Step 1: Identify User Intent
Parse the user's message for these keywords and route accordingly:
| User says | Route to |
|---|
| "pre-task", "pretask", "pre task", "Pre-Task", "2026 pre" | tasks/pretask-2026/GUIDE.md |
| "task 1", "task1", "metal artifact", "metal segmentation", "2026 task1" | tasks/task1-2026/GUIDE.md |
| "task 2", "task2", "2026 task2" | tasks/task2-2026/GUIDE.md |
| "task 3", "task3", "2026 task3" | tasks/task3-2026/GUIDE.md |
| "2025", "sts2025", "last year", "CBCT pulp" | tasks/sts2025/GUIDE.md |
| "2024", "sts2024", "instance segmentation" | tasks/sts2024/GUIDE.md |
| "2023", "sts2023", "semi-supervised" | tasks/sts2023/GUIDE.md |
| "evaluate", "่ฏไผฐ", "dice", "score" | Use scripts/evaluate.py |
| "download", "ไธ่ฝฝ", "data", "ๆฐๆฎ" | Use scripts/download_data.py |
| "submit", "ๆไบค", "package", "ๆๅ
" | Use scripts/prepare_submit.py |
| "help", "ๅธฎๅฉ", "what can you do", "่ๅ" | Show menu (below) |
| Unclear / general | Show menu (below) |
Step 2: Load the Guide
Once you identify the task, read the corresponding GUIDE.md file. The guide contains step-by-step instructions that you should follow to help the user complete their task.
Step 3: Execute
Follow the guide's instructions. For scripts, use the appropriate tool (Bash) to run them. For code tasks, write/modify code as instructed.
Menu (show when intent is unclear)
When the user's intent is unclear, present this menu:
๐ STS Challenge Skill - What would you like to do?
๐ 2026 Tasks:
1. Pre-Task - 2D segmentation (UNet, ~1 hour)
2. Task 1 - CBCT segmentation under metal artifacts
3. Task 2 - CBCT-IOS registration (PointNet)
4. Task 3 - 3D CBCT teeth segmentation (3D UNet)
๐ Historical:
5. STS 2025 - 3D CBCT pulp segmentation
6. STS 2024 - Instance segmentation
7. STS 2023 - 2D teeth segmentation (semi-supervised)
๐ง Tools:
8. Download data
9. Evaluate predictions
10. Prepare submission
Just tell me the number or describe what you need!
Shared Workflows
These patterns are used across multiple tasks. Reference them from the guides.
Environment Check
Before any task, verify the user's environment:
python --version
python -c "import torch; print(f'PyTorch {torch.__version__}')"
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
Data Download
Use scripts/download_data.py to download data from the fastest available source:
python scripts/download_data.py --task <task-name> --output ./data
Supported tasks: pretask-2026
Local Evaluation
Use scripts/evaluate.py to check prediction quality before submitting:
python scripts/evaluate.py --pred <predicted_masks_dir> --gt <ground_truth_dir>
Submission Packaging
Use scripts/prepare_submit.py to create a Codabench-compatible zip:
python scripts/prepare_submit.py --masks <masks_dir> --task <task-name>
Data Sources
Pre-Task data is available from:
Competition Links
References
For deeper understanding, read:
references/algorithms.md โ UNet, semi-supervised learning, loss functionsreferences/competition-history.md โ STS 2023-2026 overviewreferences/faq.md โ Common questions and troubleshooting
