histolab

Process whole slide images for tissue detection and tile extraction.

Updated Mar 10, 2026
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill histolab-felixboehm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/histolab
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill histolab-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the complex and time-consuming process of analyzing large digital pathology slides, enabling efficient tissue detection, tile extraction, and preprocessing for machine learning.

Core Features & Use Cases

  • Slide Loading & Inspection: Easily load and inspect WSI properties.
  • Tissue Detection & Masking: Automatically identify and segment tissue regions.
  • Flexible Tile Extraction: Extract tiles using random, grid, or score-based methods.
  • Image Filtering & Preprocessing: Apply various filters for stain normalization and artifact removal.
  • Visualization Tools: Preview masks, tile locations, and extraction results.
  • Use Case: Prepare a dataset for training a cancer detection model by extracting 512x512 pixel tiles from all tumor regions across hundreds of H&E stained slides.

Quick Start

Use the histolab skill to extract 100 random 512x512 tiles from the slide located at '/path/to/your/slide.svs'.

Frequently Asked Questions about histolab

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

FAQPage Schema
How do I extract tiles from whole slide images for machine learning training?

To extract tiles from whole slide images, you can use automated tissue detection combined with flexible tile extraction strategies like random, grid, or score-based methods to generate datasets for machine learning applications.

Can I automatically detect and segment tissue regions in digital pathology slides?

Automated tissue detection and segmentation in digital pathology slides is supported through mask generation, allowing you to identify tissue regions and filter out artifacts before extracting tiles.

Does this whole slide imaging tool support various WSI formats and OpenSlide?

Whole slide imaging processing supports various WSI formats and utilizes Python libraries like OpenSlide and OpenCV for slide loading, image preprocessing, and stain normalization.

What is the best way to preprocess H&E stained slides and remove artifacts?

The best way to preprocess H&E stained slides is by applying image filtering techniques for stain normalization and artifact removal, ensuring clean tissue tiles are extracted for model training.

How do I visualize tile extraction locations and tissue masks on pathology slides?

You can visualize tile extraction locations and tissue masks using built-in visualization tools that preview masks, tile locations, and extraction results directly on the pathology slide images.