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Dicom Segmentation Api

Name: Dicom Segmentation Api
Author: sunshine-del-ux

作者 Sunshine-del-ux · GitHub ↗ · v1.0.0

cross-platform ⚠ suspicious

298

总下载

当前安装

版本数

在 OpenClaw 中安装

/install dicom-segmentation-api

功能描述

Deploy and manage medical image segmentation using TotalSegmentator and MONAI with DICOM upload, batch processing, 3D export, and statistics generation.

使用说明 (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 segmentation
GET /api/task/{task_id} - Get task status
GET /api/result/{task_id} - Get segmentation result
GET /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

安全使用建议

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.

功能分析

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.

能力评估

⚠ 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.

ℹ 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.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install dicom-segmentation-api
安装完成后，直接呼叫该 Skill 的名称或使用 /dicom-segmentation-api 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

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.

元数据

Slug dicom-segmentation-api

版本 1.0.0

许可证 —

累计安装 1

当前安装数 1

历史版本数 1

常见问题