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MarioPasc
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preprocess-debug

Diagnose medical image preprocessing failures by analyzing step artifacts, PNGs, and logs.

Updated Nov 15, 2025
Search Tags:#debugging#data quality#pipeline#error analysis#preprocessing#medical imaging
One-click install
npx skills add https://github.com/MarioPasc/MenGrowth --skill preprocess-debug
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: preprocess-debug
Source: https://github.com/MarioPasc/MenGrowth/tree/main/.claude/skills/preprocess-debug
Command: npx skills add https://github.com/MarioPasc/MenGrowth --skill preprocess-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill helps users quickly identify and resolve issues within a complex medical image preprocessing pipeline, preventing data quality problems and saving significant debugging time.

Core Features & Use Cases

  • Step Failure Identification: Pinpoints the exact preprocessing step that caused a failure or produced poor results.
  • Artifact Analysis: Guides users on how to inspect intermediate files and visualizations generated by each step.
  • Specific Failure Patterns: Provides targeted advice for common issues in registration, skull stripping, intensity normalization, and more.
  • Use Case: When a preprocessing run for a new patient fails unexpectedly, use this Skill to systematically check logs, visualizations, and artifacts to determine if the registration step misaligned the images or if skull stripping removed essential brain tissue.

Quick Start

Use the preprocess-debug skill to investigate why the registration step failed for patient MenGrowth-XXXX.

Frequently Asked Questions about preprocess-debug

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

FAQPage Schema
How do I diagnose medical image preprocessing pipeline failures?▼

Identify the failing preprocessing step by systematically examining step artifacts, visualization PNGs, and log files to trace data quality issues like misalignment or poor skull stripping.

Why does skull stripping remove essential brain tissue in medical imaging?▼

Skull stripping removes essential brain tissue when intensity normalization or bias field correction steps fail, which you can verify by inspecting intermediate visualization artifacts and log files.

How do I fix image registration misalignment in a preprocessing pipeline?▼

Fix image registration misalignment by analyzing intermediate step artifacts and visualization PNGs from the failed run to identify if data harmonization or resampling caused the error.

Can I debug bias field correction errors using visualization PNGs?▼

Debug bias field correction errors by examining visualization PNGs and log files generated during the preprocessing workflow to detect specific failure patterns in intensity normalization.

What is the best way to troubleshoot data harmonization issues in medical imaging?▼

Troubleshoot data harmonization issues by analyzing step artifacts and logs from the preprocessing pipeline to identify exact failure patterns and debug specific code locations within the workflow.

When should I check intermediate files during a preprocessing pipeline failure?▼

Check intermediate files and visualizations immediately when a preprocessing run fails unexpectedly to determine if resampling, registration, or skull stripping produced poor data quality results.

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