{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "victorbutoi-flexray--segment-xrays-from-claude-code-with-mcp",
 "repo": "VictorButoi/FleXray",
 "repo_url": "https://github.com/VictorButoi/FleXray",
 "repo_slug": "victorbutoi-flexray",
 "title": "Ask Claude Code to segment X-rays through Fle Xray MCP",
 "page_title": "How to ask Claude Code to segment X-rays through Fle Xray MCP with Fle Xray",
 "summary": "Register the fxr-mcp server so an AI client can run segment_image and inspect label protocols.",
 "who": "A developer or analyst who already works in Claude Code or Claude Desktop and wants to run X-ray segmentation through natural language.",
 "problem": "Switching between a chat assistant and a terminal to run segmentation and read results slows down analysis of a batch of images.",
 "outcome": "An AI client that calls segment_image on a named X-ray and reports per-label statistics, plus lookups of available models and protocols.",
 "steps": [
  "Install the MCP extra with `python -m pip install \"flexray[mcp]\"`.",
  "Register the server with `claude mcp add flexray -- fxr-mcp`.",
  "Start Claude Code and ask it to use the flexray tools on your image path.",
  "Check that the reply includes per-label statistics from segment_image, then open the saved output files to confirm them."
 ],
 "agent_prompt": "Using the FleXray MCP server from the FleXray repo, call segment_image on <image-path> and report the per-label statistics. Expect a summary of each anatomical label and the output files. Verify the label names match the label_names.json written by the run.",
 "agents": [
  "claude-code"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "The README names the MCP tools but does not list their parameters, so check docs/mcp.md before scripting calls beyond the natural-language use shown.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.10 or newer and pip",
  "One or more X-ray images in PNG, JPEG, TIFF, BMP, or DICOM (DICOM needs the dicom extra)",
  "Internet access to download pretrained weights from Hugging Face on first run",
  "For training only: a labeled dataset, and a GPU or CPU for the dry run; the README says training is single-process with at most one GPU",
  "For the MCP use case: an MCP-capable client such as Claude Code or Claude Desktop"
 ],
 "risk": "sensitive",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/victorbutoi-flexray/segment-xrays-from-claude-code-with-mcp/",
 "md": "https://dedupedai.com/use-cases/victorbutoi-flexray/segment-xrays-from-claude-code-with-mcp/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/victorbutoi-flexray--segment-xrays-from-claude-code-with-mcp.json",
 "api_version": "v1"
}