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histolab

Extract and preprocess whole slide image tiles with tissue detection and stain normalization.

13|3|Updated Jun 10, 2026
Search Tags:#stain normalization#histology#tile extraction#tissue detection#whole slide image
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill histolab-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/histolab
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill histolab-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires histolab, pooch, numpy, opencv-python, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the process of extracting and preprocessing tiles from whole slide images (WSI) for H&E images, enabling efficient analysis and preparation for deep learning pipelines.

Core Features & Use Cases

  • WSI Tile Extraction: Extract informative tiles from gigapixel images for analysis.
  • Tissue Detection: Automatically identify tissue regions and filter background artifacts.
  • Stain Normalization: Standardize staining appearance across slides for consistency.
  • Use Case: With this Skill, you can quickly preprocess a whole slide image, extract tiles based on tissue detection, and normalize the staining for better analysis.

Quick Start

Extract tiles from 'slide.svs' using the histolab skill with the following command: extract_tiles slide.svs --tile_size 512 --n_tiles 100

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 histology analysis?▼

You can extract tiles from whole slide images (WSI) using automated tissue detection to filter background artifacts. This Skill outputs standardized tissue tiles ready for deep learning pipelines.

What is stain normalization and why is it needed for H&E images?▼

Stain normalization standardizes the coloring appearance across different H&E histology slides. It ensures visual consistency across various scanners and laboratories, resulting in cleaner datasets for accurate digital pathology analysis.

Does histolab tile extraction require OpenSlide and specific Python versions?▼

Yes, this WSI tile extraction requires Python 3.8-3.11 and OpenSlide installed. Setting up this environment correctly ensures successful tissue detection and processing of various WSI formats.

How do I automatically detect tissue regions and filter out background artifacts?▼

Automatically detecting tissue regions filters out empty background and artifacts on whole slide images. This Skill uses tissue segmentation to identify valid tissue, yielding clean tiles for dataset preparation.

Can I prepare a dataset from svs files for deep learning pipelines?▼

Yes, you can prepare a dataset from svs files by preprocessing whole slide images and extracting tiles based on tissue detection. This yields normalized tile-based datasets directly ready for deep learning models.

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