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nv-segment-ct-finetune

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

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

NV Segment-CT Finetune provides an auto-configured wrapper around the upstream MONAI-based finetuning workflow for NV-Segment-CT, enabling researchers to reliably run smoke tests and MSD Task06 sanity runs without manually wiring the bundle.

Core Features & Use Cases

  • Auto-configured wrapper around NV-Segment-CT continual finetune entry, coordinating dataset inputs, patch sizes, and GPU/runtime settings.
  • Supports smoke (spleen_micro) and sanity (Task06_Lung) presets, with explicit label mappings (e.g., [[1, 23]]) and automatic class-prompt segmentation.
  • Exposes outputs finetuned_ckpt and result_json and relies on the upstream bundle for the heavy lifting.

Quick Start

Run the finetune script against a dataset to produce a finetuned model and an evaluation artifact.

Frequently Asked Questions about nv-segment-ct-finetune

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

FAQPage Schema
How do I finetune a MONAI model for CT segmentation without manually wiring the bundle?▼

To finetune a MONAI model for CT segmentation without manual wiring, use this auto-configured wrapper to coordinate dataset inputs, patch sizes, and GPU settings, relying on the upstream bundle for the heavy lifting.

What datasets and presets are supported for MONAI continual finetuning on CT scans?▼

MONAI continual finetuning on CT scans supports smoke tests using the spleen_micro preset and sanity runs using the Task06_Lung preset, with explicit label mappings and automatic class-prompt segmentation.

What outputs do I get after running a CT segmentation finetuning workflow?▼

After running a CT segmentation finetuning workflow, you receive a finetuned_ckpt checkpoint file and a result_json evaluation artifact, generated by executing the finetune script against your dataset.

Do I need a specific runtime environment to run automatic CT segmentation finetuning?▼

Yes, automatic CT segmentation finetuning requires a compatible MONAI runtime and a resolved bundle containing train.json, train_continual.json, and evaluate.json files to execute successfully.

Can I run sanity checks on MSD Task06 Lung datasets using MONAI bundles?▼

Yes, you can run sanity checks on MSD Task06 Lung datasets using MONAI bundles through the auto-configured sanity preset, which handles automatic class mappings and optional automatic segmentation.

Why does my MONAI finetune workflow fail without a resolved bundle configuration?▼

A MONAI finetune workflow fails without a resolved bundle because the auto-configured wrapper requires train.json, train_continual.json, and evaluate.json configurations to orchestrate the end-to-end finetuning process.

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