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histolab

Automate tissue detection and tile extraction from whole slide images.

1|1|Updated Mar 13, 2026
Search Tags:#deep learning#image processing#histology#whole slide imaging#digital pathology#tile extraction
One-click install
npx skills add https://github.com/youyinnn/skills-collection --skill histolab-youyinnn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/youyinnn/skills-collection/tree/main/plugins/data-preparation-and-processing/skills/histolab
Command: npx skills add https://github.com/youyinnn/skills-collection --skill histolab-youyinnn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openslide-python, opencv-python, scikit-image, pillow, numpy, pandas, matplotlib, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex and time-consuming process of extracting meaningful data from large whole slide pathology images (WSIs), enabling efficient digital pathology workflows.

Core Features & Use Cases

  • WSI Loading & Inspection: Load, view metadata, and generate thumbnails for various WSI formats.
  • Tissue Detection & Masking: Automatically identify and segment tissue regions, excluding background and artifacts.
  • Tile Extraction: Extract small, standardized image tiles from WSIs using random, grid, or score-based strategies.
  • Image Filtering & Preprocessing: Apply various filters for stain normalization, artifact removal, and quality control.
  • Use Case: A researcher needs to train a deep learning model on histopathology slides. This Skill can automatically extract thousands of representative tissue tiles from hundreds of slides, preprocess them, and prepare them for model training.

Quick Start

Use the histolab skill to extract 100 random 512x512 pixel 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 deep learning training?▼

You can extract tiles from whole slide images by applying automated tissue detection and utilizing random, grid, or score-based tiling strategies to produce standardized tiles for deep learning model training.

What is the best way to segment tissue regions in digital pathology slides?▼

Segmenting tissue regions in digital pathology slides is achieved through automated tissue detection and masking algorithms that identify tissue while excluding background and artifacts during whole slide image processing.

Does this whole slide imaging workflow support multiple WSI formats and OpenSlide?▼

Yes, this whole slide imaging workflow supports various WSI formats and utilizes the OpenSlide library for image access, alongside OpenCV for image processing tasks like stain normalization and artifact removal.

How do I apply stain normalization and artifact removal to histology tiles?▼

To apply stain normalization and artifact removal to histology tiles, you utilize the customizable image filtering and preprocessing features during the tile extraction workflow to ensure high-quality data for model training.

Can I generate thumbnails and inspect metadata for whole slide pathology images?▼

Yes, you can generate thumbnails and inspect metadata for whole slide pathology images by using the WSI loading and inspection features to view slide details before performing tissue tile extraction.

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