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Dicom Segmentation Api
by
Sunshine-del-ux
· GitHub ↗
· v1.0.0
298
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0
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Install in OpenClaw
/install dicom-segmentation-api
Description
Deploy and manage medical image segmentation using TotalSegmentator and MONAI with DICOM upload, batch processing, 3D export, and statistics generation.
README (SKILL.md)
DICOM Segmentation API
Deploy medical image segmentation API using TotalSegmentator and MONAI.
Features
- TotalSegmentator integration (117 body structures)
- MONAI workflow support
- Fast API server
- DICOM file upload
- 3D model export (GLB format)
- Statistics generation
- Batch processing
Usage
# Start server
python api_server.py
# Or with custom port
python api_server.py --port 8000
API Endpoints
POST /api/segment- Upload DICOM for segmentationGET /api/task/{task_id}- Get task statusGET /api/result/{task_id}- Get segmentation resultGET /health- Health check
Requirements
- Python 3.8+
- CUDA (optional, for GPU acceleration)
- 8GB RAM minimum
Models
- TotalSegmentator: 117 body structures
- MONAI: whole-body-3mm, organ, tumor models
Author
Sunshine-del-ux
Usage Guidance
Do not run this bundle on a production or sensitive host yet. Specific concerns:
- The package is incomplete: api_server.py and requirements.txt referenced by SKILL.md/start.sh are missing. Ask the author for the missing files or source repository before use.
- start.sh will run 'pip install -r requirements.txt' if imports fail; without an included requirements.txt this could install unexpected packages from the host or fail. Only allow installs after reviewing a requirements list and the packages' reputations.
- Because this handles medical (DICOM) data, ensure you run it in an isolated environment (container, VM, or sandbox), enforce TLS and authentication on the API, and review data retention/logging to avoid PHI leaks.
- If you decide to test: run in an isolated VM/container, review or provide the full api_server.py and requirements.txt, pin package versions, and audit network communications and filesystem writes (the script creates an 'output' directory).
Providing the missing server code and a concrete requirements.txt would materially change this assessment toward 'benign' if those files are consistent with the claimed purpose and contain no hidden network endpoints or credential access.
Capability Analysis
Type: OpenClaw Skill
Name: dicom-segmentation-api
Version: 1.0.0
The skill bundle provides a standard setup for a medical imaging (DICOM) segmentation API using MONAI and TotalSegmentator. The documentation in SKILL.md and the execution logic in start.sh are consistent with the stated purpose and contain no evidence of malicious intent, data exfiltration, or prompt injection.
Capability Assessment
Purpose & Capability
The name and description describe a deployable API, but the package does not include the server code (api_server.py) or requirements.txt referenced by SKILL.md and start.sh. That mismatch means the bundle cannot perform the claimed function as-is and suggests either incomplete packaging or missing external downloads.
Instruction Scope
SKILL.md tells the agent to run python api_server.py and start.sh does exactly that; neither the instructions nor the script ask for unexpected system files or credentials. However start.sh will attempt to import fastapi/torch/monai and, on failure, runs 'pip install -r requirements.txt' (a broad operation) — the script thus has scope to modify the environment and install arbitrary Python packages not provided in the bundle.
Install Mechanism
There is no explicit install spec, but start.sh implicitly installs dependencies via pip from a requirements.txt that is not present. That means the runtime may perform an unreviewed package installation from whatever requirements file exists on the host or fail; automatic pip installs without an included requirements list are disproportionate and risky.
Credentials
The skill declares no required environment variables or credentials, which is appropriate in that nothing obvious requires secrets. However handling DICOM/medical images implies sensitive data (PHI); the package provides no guidance on authentication, encryption, or access controls, and will create an output directory and start a network service—these are operationally significant and should be justified/configured by the user.
Persistence & Privilege
The skill is not marked always:true and does not request persistent platform privileges. The included start.sh writes a local 'output' directory and may install packages, but it does not attempt to modify other skills or global agent config.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install dicom-segmentation-api - After installation, invoke the skill by name or use
/dicom-segmentation-api - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of DICOM Segmentation API.
- Integrates TotalSegmentator (117 body structures) and MONAI models for medical image segmentation.
- Provides FastAPI server for handling DICOM uploads and segmentation tasks.
- Supports batch processing, 3D model export (GLB), and statistics generation.
- Includes endpoints for file upload, task status, results retrieval, and health checks.
- Runs on Python 3.8+ with optional CUDA GPU acceleration.
Metadata
Frequently Asked Questions
What is Dicom Segmentation Api?
Deploy and manage medical image segmentation using TotalSegmentator and MONAI with DICOM upload, batch processing, 3D export, and statistics generation. It is an AI Agent Skill for Claude Code / OpenClaw, with 298 downloads so far.
How do I install Dicom Segmentation Api?
Run "/install dicom-segmentation-api" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Dicom Segmentation Api free?
Yes, Dicom Segmentation Api is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Dicom Segmentation Api support?
Dicom Segmentation Api is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Dicom Segmentation Api?
It is built and maintained by Sunshine-del-ux (@sunshine-del-ux); the current version is v1.0.0.
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