# Orthanc DICOM + Nerve Segmentation MCP Server

> Orthanc DICOM + Nerve Segmentation MCP Server — shrutig1602-dcm-segmentation-mcp-server. Use this tool when you need to streamline medical imaging workflows by navigating and extracting data from Orthanc PACS systems, and automating nerve segmentation on ultrasound DICOM instances. It solves problems related to medical image analysis, report generation, and data integration, providing outputs such as segmented nerve images and uploaded series. Ideal for use in clinical and research settings where efficient image processing and analysis are crucial.

Canonical page: https://skillsregistry.net/skills/shrutig1602-dcm-segmentation-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/shrutig1602-dcm-segmentation-mcp-server

## Description

Enables navigating an Orthanc PACS, extracting PDF reports, and running nerve segmentation on ultrasound DICOM instances, with results uploaded back as new series.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** file-system
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/oo98lj1ouf)
- **Repository:** <https://github.com/shrutig1602/DCM-Segmentation-MCP-Server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "shrutig1602-dcm-segmentation-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/shrutig1602-dcm-segmentation-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/shrutig1602-dcm-segmentation-mcp-server/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt