skills.rest
sayalinvidia
sayalinvidiaCOMMUNITY
Explore all skills by this author →
GitHub

nv-segment-ctmr

Run NV-Segment-CTMR MONAI bundle segmentation and output a labeled map with JSON report.

Updated Apr 16, 2026
Search Tags:#segmentation#wrapper#monai#nv-segment-ctmr#ct_body#mri_body
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-segment-ctmr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nv-segment-ctmr
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/nv-segment-ctmr
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-segment-ctmr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nibabel, numpy, typer, and includes scripts (resource) components.

What problem does it solve?

Provides a lightweight engineering wrapper around the NV-Segment-CTMR MONAI bundle to generate a structured label-map output for a given CT or MRI volume, including provenance data.

Core Features & Use Cases

  • Thin shell that invokes python -m monai.bundle run with a defined config and input dictionary, returning a label map and a JSON report.
  • Manages inputs (ct_or_mr_volume, modality) and optional ground-truth references for verifiers; captures model inventory and upstream commit.
  • Intended for engineering verification, research QA, and automated workflow testing of segmentation pipelines.

Quick Start

Run the wrapper to segment a CT or MRI volume and emit a labeled mask and JSON report.

Frequently Asked Questions about nv-segment-ctmr

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run MONAI bundle segmentation on a CT or MRI volume and get a structured report?▼

This wrapper invokes python -m monai.bundle run with a defined config and input dictionary to generate a labeled mask and a JSON report containing provenance data for CT or MRI volumes.

What is included in the JSON report when segmenting CT and MRI volumes?▼

The JSON report from this CT and MRI segmentation wrapper captures input and output summaries, model inventory, upstream commit, and provenance data needed for engineering verification and QA workflows.

Do I need a CUDA-enabled GPU to run CT or MRI segmentation with MONAI?▼

Yes, running this MONAI bundle wrapper for CT and MRI segmentation requires a CUDA-enabled GPU, Python, and dependencies including nibabel, numpy, and typer to execute successfully.

Can I use this segmentation wrapper for automated workflow testing and QA?▼

Yes, this segmentation wrapper is designed for engineering verification, research QA, and automated workflow testing of CT and MRI segmentation pipelines, providing structured outputs and provenance data.

Does the CT and MRI segmentation wrapper support optional ground-truth references for verification?▼

Yes, the wrapper manages optional ground-truth references for verifiers alongside ct_or_mr_volume and modality inputs, enabling comparison of generated label maps against reference data during QA workflows.

Related Skills

Explore More Software Engineering Skills→
sayalinvidiasayalinvidia

nv-segment-ct-finetune

Automate end-to-end finetuning of NV-Segment-CT models using MONAI bundles.

Community
Advanced
IngvarConsultingIngvarConsulting
140

mxl-compile

Convert JSON layout definitions into 1C-compatible XML templates.

Official
Intermediate
numerainumerai
1.2k

numerai-model-implementation

Register and integrate a new Numerai model type into the agents training pipeline.

Official
Advanced
nahisahonahisaho
3

scientific-radiology-ai

Build explainable MONAI pipelines for radiology classification, segmentation, and reporting.

Community
Advanced