A robust FastAPI-based service for automated medical image segmentation using TotalSegmentator and MONAI frameworks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dicom-segmentation-api
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dicom-segmentation-api using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is DICOM Segmentation API?
The DICOM Segmentation API is a high-performance tool designed to bring advanced medical imaging capabilities to your development environment. By leveraging Openclaw Skills, this tool enables developers to deploy a local or cloud-based server that automates the identification of 117 body structures using the TotalSegmentator library. It acts as a bridge between raw medical data and actionable clinical insights, providing a seamless interface for medical image processing.
Built on FastAPI, the skill supports complex MONAI workflows and provides comprehensive outputs, including 3D model exports in GLB format and detailed statistical analysis. This integration ensures that medical researchers and developers can focus on analysis rather than the underlying infrastructure of medical AI models.
DICOM Segmentation API Use Cases
- Automated identification and segmentation of 117 distinct body structures from CT scans.
- Generation of 3D GLB models for anatomical visualization and surgical planning.
- Batch processing of large DICOM datasets for clinical research and data analysis.
- Integration of AI-driven radiology workflows into existing health-tech platforms using Openclaw Skills.
How DICOM Segmentation API Works
- The user starts the FastAPI server which acts as the primary interface for medical image requests.
- DICOM files are uploaded to the API, triggering an asynchronous segmentation task.
- The backend utilizes TotalSegmentator or MONAI models to process the volumetric data.
- Segmented masks and 3D models are generated, along with volumetric statistics.
- The client polls the task status and retrieves the final results once processing is complete.
DICOM Segmentation API Setup
To get started with this skill, ensure you have Python 3.8 or higher installed. It is recommended to have a CUDA-compatible GPU for faster inference.
# Install dependencies and start the server
python api_server.py --port 8000
DICOM Segmentation API Data Schema & Taxonomy
The skill manages medical data through a structured pipeline. Below is the primary data organization:
| Component | Type | Description |
|---|---|---|
| Input | DICOM | Standard medical imaging files (CT/MRI) |
| Segmentation | NIfTI | High-resolution voxel masks |
| Visualization | GLB | 3D models for web-based or AR viewing |
| Metadata | JSON | Task status, timestamps, and body structure statistics |
DICOM Segmentation API Advanced Features
- Multi-model support including TotalSegmentator and specific MONAI organ/tumor models.
- Asynchronous task queuing for efficient handling of large medical volumes.
- GPU acceleration via CUDA for rapid inference in production environments.
- Seamless integration with Openclaw Skills for automated medical data pipelines.
SKILL.md
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